Artículos de revistas: "Spatio-temporal trajectories"

1

Gudmundsson, Joachim, Jyrki Katajainen, Damian Merrick, Cahya Ong, and Thomas Wolle. "Compressing spatio-temporal trajectories." Computational Geometry 42, no. 9 (2009): 825–41. http://dx.doi.org/10.1016/j.comgeo.2009.02.002.

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2

Bao, Wei, Li Xin Ji, Shi Lin Gao, Xing Li, and Li Xiong Liu. "Video Copy Detection Based on Fusion of Spatio-Temporal Features." Applied Mechanics and Materials 347-350 (August 2013): 3653–61. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3653.

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A video copy detection method based on fusion of spatio-temporal features is proposed in this paper. Firstly, trajectories are built and lens boundaries are detected by SURF features analyzing, then normalized histogram is used to describe spatio-temporal behavior of trajectories, the bag of visual words is constructed by trajectories behavior clustering, word frequency vectors and SURF features with behavior labels are extracted to express spatio-temporal content of lens, finally, duplicates are detected efficiently based on grade-match. The experimental results show the performance of this method is improved greatly compared with other similar methods.

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3

Zhang, Ran, Xiaohui Chen, Lin Ye, Wentao Yu, Bing Zhang, and Junnan Liu. "Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix." Applied Sciences 14, no. 10 (2024): 4104. http://dx.doi.org/10.3390/app14104104.

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This study proposes a vessel position prediction method using attention spatiotemporal graph convolutional networks, which addresses the issue of low prediction accuracy due to less consideration of inter-feature dependencies in current vessel trajectory prediction methods. First, the method cleans the vessel trajectory data and uses the Time-ratio trajectory compression algorithm to compress the trajectory data, avoiding data redundancy and providing feature points for vessel trajectories. Second, the Spectral Temporal Graph Neural Network (StemGNN) extracts the correlation matrix that describes the relationship between multiple variables as a priori matrix input to the prediction model. Then the vessel trajectory prediction model is constructed, and the attention mechanism is added to the spatial and temporal dimensions of the trajectory data based on the spatio-temporal graph convolutional network at the same time as the above operations are performed on different time scales. Finally, the features extracted from different time scales are fused through the full connectivity layer to predict the future trajectories. Experimental results show that this method achieves higher accuracy and more stable prediction results in trajectory prediction. The attention-based spatio-temporal graph convolutional networks effectively capture the spatio-temporal correlations of the main features in vessel trajectories, and the spatio-temporal attention mechanism and graph convolution have certain interpretability for the prediction results.

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4

Ni, Jinfeng, and Chinya V. Ravishankar. "Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations." IEEE Transactions on Knowledge and Data Engineering 19, no. 5 (2007): 663–78. http://dx.doi.org/10.1109/tkde.2007.1006.

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5

Sandu Popa, Iulian, Karine Zeitouni, Vincent Oria, and Ahmed Kharrat. "Spatio-temporal compression of trajectories in road networks." GeoInformatica 19, no. 1 (2014): 117–45. http://dx.doi.org/10.1007/s10707-014-0208-4.

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6

Zhang, Dongzhi, Kyungmi Lee, and Ickjai Lee. "Semantic periodic pattern mining from spatio-temporal trajectories." Information Sciences 502 (October 2019): 164–89. http://dx.doi.org/10.1016/j.ins.2019.06.035.

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7

Wang, Jiang, Cheng Zhu, Yun Zhou, and Weiming Zhang. "Vessel Spatio-temporal Knowledge Discovery with AIS Trajectories Using Co-clustering." Journal of Navigation 70, no. 6 (2017): 1383–400. http://dx.doi.org/10.1017/s0373463317000406.

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Large volumes of data collected by the Automatic Identification System (AIS) provide opportunities for studying both single vessel motion behaviours and collective mobility patterns on the sea. Understanding these behaviours or patterns is of great importance to maritime situational awareness applications. In this paper, we leveraged AIS trajectories to discover vessel spatio-temporal co-occurrence patterns, which distinguish vessel behaviours simultaneously in terms of space, time and other dimensions (such as ship type, speed, width etc.). To this end, available AIS data were processed to generate spatio-temporal matrices and spatio-temporal tensors (i.e., multidimensional arrays). We then imposed a sparse bilinear decomposition on the matrices and a sparse multi-linear decomposition on the tensors. Experimental results on a real-world dataset demonstrated the effectiveness of this methodology, with which we show the existence of connection among regions, time, and vessel attributes.

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8

Arslan, Muhammad, Christophe Cruz, Ana-Maria Roxin, and Dominique Ginhac. "Spatio-temporal analysis of trajectories for safer construction sites." Smart and Sustainable Built Environment 7, no. 1 (2018): 80–100. http://dx.doi.org/10.1108/sasbe-10-2017-0047.

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Purpose The purpose of this paper is to improve the safety of construction workers by understanding their behaviors on construction sites using spatio-temporal (ST) trajectories. Design/methodology/approach A review of construction safety management literature and international occupational health and safety statistics shows that the major reasons for fatalities on construction sites are mobility-related issues, such as unsafe human behaviors, difficult site conditions, and workers falling from heights and striking against or being struck by moving objects. Consequently, literature has been reviewed to find possible technological solutions to track the mobility of construction workers to reduce fatalities. This examination has suggested that location acquisition systems, such as Global Positioning System (GPS), have been widely used for real-time monitoring and tracking of workers on construction sites for hazard prevention. However, the raw data captured from GPS devices are generally available as discrete points and do not hold enough information to understand the workers’ mobility. As a solution, an application to transform raw GPS data into ST trajectories using different preprocessing algorithms is proposed for enhancing worker safety on construction sites. Findings The proposed system preprocesses raw GPS data for stay point detection, trajectory segmentation and intersection of multiple trajectories to find significant places and movements of workers on a construction site to enhance the information available to H&S managers for decision-making processes. In addition, it reduces the size of trajectory data for future analyses. Originality/value Application of location acquisition systems for construction safety management is very well addressed in the existing literature. However, a significant gap has been found: the usage of preprocessed ST trajectories is still missing in workers’ safety monitoring scenarios in the area of construction management. To address this research gap, the proposed system uses preprocessed ST trajectories to monitor workers’ movements on a construction site to identify potentially unsafe behaviors.

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9

Zhang, Chengcui. "A Survey of Visual Traffic Surveillance Using Spatio-Temporal Analysis and Mining." International Journal of Multimedia Data Engineering and Management 4, no. 3 (2013): 42–60. http://dx.doi.org/10.4018/jmdem.2013070103.

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The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effectively analyze these spatio-temporal data to automatically find its inherent characteristics for different visual traffic surveillance applications has been of great interest. Examples of spatio-temporal patterns extracted from traffic surveillance videos include, but are not limited to, sudden stops, harsh turns, speeding, and collisions. To meet the different needs of various traffic surveillance applications, several application- or event- specific models have been proposed in the literature. This paper provides a survey of different models and data mining algorithms to cover state of the art in spatio-temporal modelling, spatio-temporal data mining, and spatio-temporal retrieval for traffic surveillance video databases. In addition, the database model issues and challenges for traffic surveillance videos are also discussed in this survey.

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10

Boulmakoul, Azedine. "Moving Object Trajectories Meta-Model and Spatio-Temporal Queries." International Journal of Database Management Systems 4, no. 2 (2012): 35–54. http://dx.doi.org/10.5121/ijdms.2012.4203.

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11

Sailer, Christian, Peter Kiefer, Joram Schito, and Martin Raubal. "Map-based Visual Analytics of Moving Learners." International Journal of Mobile Human Computer Interaction 8, no. 4 (2016): 1–28. http://dx.doi.org/10.4018/ijmhci.2016100101.

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Location-based mobile learning (LBML) is a type of mobile learning in which the learning content is related to the location of the learner. The evaluation of LBML concepts and technologies is typically performed using methods known from classical usability engineering, such as questionnaires or interviews. In this paper, the authors argue for applying visual analytics to spatial and spatio-temporal visualizations of learners' trajectories for evaluating LBML. Visual analytics supports the detection and interpretation of spatio-temporal patterns and irregularities in both, single learners' as well as multiple learners' trajectories, thus revealing learners' typical behavior patterns and potential problems with the LBML software, hardware, the didactical concept, or the spatial and temporal embedding of the content.

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12

Tedjopurnomo, David Alexander, Xiucheng Li, Zhifeng Bao, Gao Cong, Farhana Choudhury, and A. K. Qin. "Similar Trajectory Search with Spatio-Temporal Deep Representation Learning." ACM Transactions on Intelligent Systems and Technology 12, no. 6 (2021): 1–26. http://dx.doi.org/10.1145/3466687.

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Similar trajectory search is a crucial task that facilitates many downstream spatial data analytic applications. Despite its importance, many of the current literature focus solely on the trajectory’s spatial similarity while neglecting the temporal information. Additionally, the few papers that use both the spatial and temporal features based their approach on a traditional point-to-point comparison. These methods model the importance of the spatial and temporal aspect of the data with only a single, pre-defined balancing factor for all trajectories, even though the relative spatial and temporal balance can change from trajectory to trajectory. In this article, we propose the first spatio-temporal, deep-representation-learning-based approach to similar trajectory search. Experiments show that utilizing both features offers significant improvements over existing point-to-point comparison and deep-representation-learning approach. We also show that our deep neural network approach is faster and performs more consistently compared to the point-to-point comparison approaches.

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13

Bicakci, Yunus Serhat, Dursun Zafer Seker, and Hande Demirel. "Location-Based Analyses for Electronic Monitoring of Parolees." ISPRS International Journal of Geo-Information 9, no. 5 (2020): 296. http://dx.doi.org/10.3390/ijgi9050296.

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This study analyses the spatio-temporal pattern of parolees using electronic monitoring, where the developed spatial framework supports the Environmental Criminology concepts such as crime patterns or crime attractive locations. A grid-based solution for spatio-temporal analyses is introduced to ensure the anonymity of the parolees. In order to test these developed concepts, the Istanbul Metropolitan Area was selected as the pilot study area. Following the developed concepts of the Crime Pattern Theory, a spatial framework was designed. A novel grid-based weighted algorithm for the most attractive areas was generated via spatial point-of-interest data and a conducted survey among police officers. The designed framework and the spatio-temporal analyses were carried out for 77 parolees using geostatistical methods. The major findings of the study are (a) 24-hour trajectories of each parolee was monitored; (b) the most attractive grids within the city were defined; and (c) for each parolee, the entrance time to the grid and the time spent within that grid were reported and analyzed. This study is a preliminary study for spatio-temporal detection of parolees’ trajectories, where location-based analyses serve well. This study aims to aid decision-makers to better monitor the parolees and justify the benefits of surveillance.

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14

Wu, Z., C. Li, Y. Wu, F. Xiao, L. Zhu, and J. Shen. "TRAVEL TIME ESTIMATION USING SPATIO-TEMPORAL INDEX BASED ON CASSANDRA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 235–42. http://dx.doi.org/10.5194/isprs-annals-iv-4-235-2018.

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Abstract. Travel time estimation plays an important role in traffic monitoring and route planning. Taxicabs equipped with Global Positioning System (GPS) devices have been frequently used to monitor the traffic state, and GPS trajectories of taxicabs also used to estimate path travel time in an urban area. However, in most cases, it is difficult to find a trajectory that fits perfectly with the query path, as some road segments may be traveled by no taxicab in present time slot. This makes it hard to estimate the travel time of the query path. This paper proposes a framework to estimate the travel time of a path by using the GPS trajectories of taxicabs as well as map data sources. In this framework, the travel time is represented as a series of residence time in cells (one cell is the gird segmentation unit), thus the key issues of the estimation are: finding the local traffic patterns of frequently shared paths from historical data and computing the stay time in cells. There are three major processes in this framework: trajectories preprocessing, establishing the temporal-spatial index and cell-based travel time estimation. Based on the temporal-spatial index, an algorithm is developed that uses similar route patterns, the cell-based travel time over a period of history and road network information to estimate the travel time of a path. This paper uses GPS trajectories of 10,357 taxicabs over a period of one week to evaluate the framework. The results demonstrate that this paper’s method is effective and feasible in city-wide scenarios.

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15

Wu, Tao, Huiqing Shen, Jianxin Qin, and Longgang Xiang. "Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features." Sustainability 13, no. 2 (2021): 690. http://dx.doi.org/10.3390/su13020690.

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Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

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Busch, S., T. Schindler, T. Klinger, and C. Brenner. "ANALYSIS OF SPATIO-TEMPORAL TRAFFIC PATTERNS BASED ON PEDESTRIAN TRAJECTORIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 497–503. http://dx.doi.org/10.5194/isprs-archives-xli-b2-497-2016.

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For driver assistance and autonomous driving systems, it is essential to predict the behaviour of other traffic participants. Usually, standard filter approaches are used to this end, however, in many cases, these are not sufficient. For example, pedestrians are able to change their speed or direction instantly. Also, there may be not enough observation data to determine the state of an object reliably, e.g. in case of occlusions. In those cases, it is very useful if a prior model exists, which suggests certain outcomes. For example, it is useful to know that pedestrians are usually crossing the road at a certain location and at certain times. This information can then be stored in a map which then can be used as a prior in scene analysis, or in practical terms to reduce the speed of a vehicle in advance in order to minimize critical situations. In this paper, we present an approach to derive such a spatio-temporal map automatically from the observed behaviour of traffic participants in everyday traffic situations. In our experiments, we use one stationary camera to observe a complex junction, where cars, public transportation and pedestrians interact. We concentrate on the pedestrians trajectories to map traffic patterns. In the first step, we extract trajectory segments from the video data. These segments are then clustered in order to derive a spatial model of the scene, in terms of a spatially embedded graph. In the second step, we analyse the temporal patterns of pedestrian movement on this graph. We are able to derive traffic light sequences as well as the timetables of nearby public transportation. To evaluate our approach, we used a 4 hour video sequence. We show that we are able to derive traffic light sequences as well as time tables of nearby public transportation.

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17

Graser, Anita. "Evaluating Spatio-temporal Data Models for Trajectories in PostGIS Databases." GI_Forum 1 (2018): 16–33. http://dx.doi.org/10.1553/giscience2018_01_s16.

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Busch, S., T. Schindler, T. Klinger, and C. Brenner. "ANALYSIS OF SPATIO-TEMPORAL TRAFFIC PATTERNS BASED ON PEDESTRIAN TRAJECTORIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 497–503. http://dx.doi.org/10.5194/isprsarchives-xli-b2-497-2016.

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For driver assistance and autonomous driving systems, it is essential to predict the behaviour of other traffic participants. Usually, standard filter approaches are used to this end, however, in many cases, these are not sufficient. For example, pedestrians are able to change their speed or direction instantly. Also, there may be not enough observation data to determine the state of an object reliably, e.g. in case of occlusions. In those cases, it is very useful if a prior model exists, which suggests certain outcomes. For example, it is useful to know that pedestrians are usually crossing the road at a certain location and at certain times. This information can then be stored in a map which then can be used as a prior in scene analysis, or in practical terms to reduce the speed of a vehicle in advance in order to minimize critical situations. In this paper, we present an approach to derive such a spatio-temporal map automatically from the observed behaviour of traffic participants in everyday traffic situations. In our experiments, we use one stationary camera to observe a complex junction, where cars, public transportation and pedestrians interact. We concentrate on the pedestrians trajectories to map traffic patterns. In the first step, we extract trajectory segments from the video data. These segments are then clustered in order to derive a spatial model of the scene, in terms of a spatially embedded graph. In the second step, we analyse the temporal patterns of pedestrian movement on this graph. We are able to derive traffic light sequences as well as the timetables of nearby public transportation. To evaluate our approach, we used a 4 hour video sequence. We show that we are able to derive traffic light sequences as well as time tables of nearby public transportation.

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19

Wang, Shengsheng, Dayou Liu, Changji Wen, Weiwei Liu, and Yong Lai. "Interactive Activity Learning from Trajectories with Qualitative Spatio-Temporal Relation." Chinese Journal of Electronics 24, no. 3 (2015): 508–12. http://dx.doi.org/10.1049/cje.2015.07.012.

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20

Sighencea, Bogdan Ilie, Ion Rareș Stanciu, and Cătălin Daniel Căleanu. "D-STGCN: Dynamic Pedestrian Trajectory Prediction Using Spatio-Temporal Graph Convolutional Networks." Electronics 12, no. 3 (2023): 611. http://dx.doi.org/10.3390/electronics12030611.

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Predicting pedestrian trajectories in urban scenarios is a challenging task that has a wide range of applications, from video surveillance to autonomous driving. The task is difficult since pedestrian behavior is affected by both their individual path’s history, their interactions with others, and with the environment. For predicting pedestrian trajectories, an attention-based interaction-aware spatio-temporal graph neural network is introduced. This paper introduces an approach based on two components: a spatial graph neural network (SGNN) for interaction-modeling and a temporal graph neural network (TGNN) for motion feature extraction. The SGNN uses an attention method to periodically collect spatial interactions between all pedestrians. The TGNN employs an attention method as well, this time to collect each pedestrian’s temporal motion pattern. Finally, in the graph’s temporal dimension characteristics, a time-extrapolator convolutional neural network (CNN) is employed to predict the trajectories. Using a lower variable size (data and model) and a better accuracy, the proposed method is compact, efficient, and better than the one represented by the social-STGCNN. Moreover, using three video surveillance datasets (ETH, UCY, and SDD), D-STGCN achieves better experimental results considering the average displacement error (ADE) and final displacement error (FDE) metrics, in addition to predicting more social trajectories.

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21

Wang, Huandong, Qiaohong Yu, Yu Liu, Depeng Jin, and Yong Li. "Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 4 (2021): 1–24. http://dx.doi.org/10.1145/3494993.

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With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application service providers (ASPs). On the other hand, the rising paradigm of knowledge graph (KG) provides us a promising solution to extract structured "knowledge" from massive trajectory data. In this paper, we focus on modeling users' spatio-temporal mobility patterns based on knowledge graph techniques, and predicting users' future movement based on the "knowledge" extracted from multiple sources in a cohesive manner. Specifically, we propose a new type of knowledge graph, i.e., spatio-temporal urban knowledge graph (STKG), where mobility trajectories, category information of venues, and temporal information are jointly modeled by the facts with different relation types in STKG. The mobility prediction problem is converted to the knowledge graph completion problem in STKG. Further, a complex embedding model with elaborately designed scoring functions is proposed to measure the plausibility of facts in STKG to solve the knowledge graph completion problem, which considers temporal dynamics of the mobility patterns and utilizes PoI categories as the auxiliary information and background knowledge. Extensive evaluations confirm the high accuracy of our model in predicting users' mobility, i.e., improving the accuracy by 5.04% compared with the state-of-the-art algorithms. In addition, PoI categories as the background knowledge and auxiliary information are confirmed to be helpful by improving the performance by 3.85% in terms of accuracy. Additionally, experiments show that our proposed method is time-efficient by reducing the computational time by over 43.12% compared with existing methods.

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CHENEVIÈRE, FREDERIC, SAMIA BOUKIR, and BERTRAND VACHON. "COMPRESSION AND RECOGNITION OF SPATIO-TEMPORAL SEQUENCES FROM CONTEMPORARY BALLET." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 05 (2006): 727–45. http://dx.doi.org/10.1142/s0218001406004880.

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We aim at recognizing a set of dance gestures from contemporary ballet. Our input data are motion trajectories followed by the joints of a dancing body provided by a motion-capture system. It is obvious that direct use of the original signals is unreliable and expensive. Therefore, we propose a suitable tool for nonuniform sub-sampling of spatio-temporal signals. The key to our approach is the use of polygonal approximation to provide a compact and efficient representation of motion trajectories. Our dance gesture recognition method involves a set of Hidden Markov Models (HMMs), each of them being related to a motion trajectory followed by the joints. The recognition of such movements is then achieved by matching the resulting gesture models with the input data via HMMs. We have validated our recognition system on 12 fundamental movements from contemporary ballet performed by four dancers.

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23

Yang, Wenguang, Kan Ren, Minjie Wan, Xiaofang Kong, and Weixian Qian. "Dynamic Multiple Object Segmentation with Spatio-Temporal Filtering." Sensors 24, no. 7 (2024): 2094. http://dx.doi.org/10.3390/s24072094.

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This article primarily focuses on the localization and extraction of multiple moving objects in images taken from a moving camera platform, such as image sequences captured by drones. The positions of moving objects in the images are influenced by both the camera’s motion and the movement of the objects themselves, while the background position in the images is related to the camera’s motion. The main objective of this article was to extract all moving objects from the background in an image. We first constructed a motion feature space containing motion distance and direction, to map the trajectories of feature points. Subsequently, we employed a clustering algorithm based on trajectory distinctiveness to differentiate between moving objects and the background, as well as feature points corresponding to different moving objects. The pixels between the feature points were then designated as source points. Within local regions, complete moving objects were segmented by identifying these pixels. We validated the algorithm on some sequences in the Video Verification of Identity (VIVID) program database and compared it with relevant algorithms. The experimental results demonstrated that, in the test sequences when the feature point trajectories exceed 10 frames, there was a significant difference in the feature space between the feature points on the moving objects and those on the background. Correctly classified frames with feature points accounted for 67% of the total frames.The positions of the moving objects in the images were accurately localized, with an average IOU value of 0.76 and an average contour accuracy of 0.57. This indicated that our algorithm effectively localized and segmented the moving objects in images captured by moving cameras.

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Khoshahval, S., M. Farnaghi, and M. Taleai. "SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 395–99. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-395-2017.

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Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.

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Zheng, Yaolin, Hongbo Huang, Xiuying Wang, Xiaoxu Yan, and Longfei Xu. "Spatio-Temporal Fusion for Human Action Recognition via Joint Trajectory Graph." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7579–87. http://dx.doi.org/10.1609/aaai.v38i7.28590.

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Graph Convolutional Networks (GCNs) and Transformers have been widely applied to skeleton-based human action recognition, with each offering unique advantages in capturing spatial relationships and long-range dependencies. However, for most GCN methods, the construction of topological structures relies solely on the spatial information of human joints, limiting their ability to directly capture richer spatio-temporal dependencies. Additionally, the self-attention modules of many Transformer methods lack topological structure information, restricting the robustness and generalization of the models. To address these issues, we propose a Joint Trajectory Graph (JTG) that integrates spatio-temporal information into a uniform graph structure. We also present a Joint Trajectory GraphFormer (JT-GraphFormer), which directly captures the spatio-temporal relationships among all joint trajectories for human action recognition. To better integrate topological information into spatio-temporal relationships, we introduce a Spatio-Temporal Dijkstra Attention (STDA) mechanism to calculate relationship scores for all the joints in JTG. Furthermore, we incorporate the Koopman operator into the classification stage to enhance the model's representation ability and classification performance. Experiments demonstrate that JT-GraphFormer achieves outstanding performance in human action recognition tasks, outperforming state-of-the-art methods on the NTU RGB+D, NTU RGB+D 120, and N-UCLA datasets.

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McGuire, M. P., V. P. Janeja, and A. Gangopadhyay. "Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets." Data Mining and Knowledge Discovery 28, no. 4 (2013): 961–1003. http://dx.doi.org/10.1007/s10618-013-0324-z.

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Inui, Norio, and Makoto Katori. "Statistical Properties of Trajectories of Friendly Walkers on Spatio-Temporal Plane." Journal of the Physical Society of Japan 70, no. 1 (2001): 78–85. http://dx.doi.org/10.1143/jpsj.70.78.

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28

Zhang, Dongzhi, Kyungmi Lee, and Ickjai Lee. "Mining hierarchical semantic periodic patterns from GPS-collected spatio-temporal trajectories." Expert Systems with Applications 122 (May 2019): 85–101. http://dx.doi.org/10.1016/j.eswa.2018.12.047.

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Govender, Divina, and Jules-Raymond Tapamo. "Spatio-Temporal Scale Coded Bag-of-Words." Sensors 20, no. 21 (2020): 6380. http://dx.doi.org/10.3390/s20216380.

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The Bag-of-Words (BoW) framework has been widely used in action recognition tasks due to its compact and efficient feature representation. Various modifications have been made to this framework to increase its classification power. This often results in an increased complexity and reduced efficiency. Inspired by the success of image-based scale coded BoW representations, we propose a spatio-temporal scale coded BoW (SC-BoW) for video-based recognition. This involves encoding extracted multi-scale information into BoW representations by partitioning spatio-temporal features into sub-groups based on the spatial scale from which they were extracted. We evaluate SC-BoW in two experimental setups. We first present a general pipeline to perform real-time action recognition with SC-BoW. Secondly, we apply SC-BoW onto the popular Dense Trajectory feature set. Results showed SC-BoW representations to successfully improve performance by 2–7% with low added computational cost. Notably, SC-BoW on Dense Trajectories outperformed more complex deep learning approaches. Thus, scale coding is a low-cost and low-level encoding scheme that increases classification power of the standard BoW without compromising efficiency.

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30

Dorosti, Ali, Ali Asghar Alesheikh, and Mohammad Sharif. "Measuring Trajectory Similarity Based on the Spatio-Temporal Properties of Moving Objects in Road Networks." Information 15, no. 1 (2024): 51. http://dx.doi.org/10.3390/info15010051.

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Advancements in navigation and tracking technologies have resulted in a significant increase in movement data within road networks. Analyzing the trajectories of network-constrained moving objects makes a profound contribution to transportation and urban planning. In this context, the trajectory similarity measure enables the discovery of inherent patterns in moving object data. Existing methods for measuring trajectory similarity in network space are relatively slow and neglect the temporal characteristics of trajectories. Moreover, these methods focus on relatively small volumes of data. This study proposes a method that maps trajectories onto a network-based space to overcome these limitations. This mapping considers geographical coordinates, travel time, and the temporal order of trajectory segments in the similarity measure. Spatial similarity is measured using the Jaccard coefficient, quantifying the overlap between trajectory segments in space. Temporal similarity, on the other hand, incorporates time differences, including common trajectory segments, start time variation and trajectory duration. The method is evaluated using real-world taxi trajectory data. The processing time is one-quarter of that required by existing methods in the literature. This improvement allows for spatio-temporal analyses of a large number of trajectories, revealing the underlying behavior of moving objects in network space.

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31

Li, Zheng, Xueyuan Huang, Chun Liu, and Wei Yang. "Spatio-Temporal Unequal Interval Correlation-Aware Self-Attention Network for Next POI Recommendation." ISPRS International Journal of Geo-Information 11, no. 11 (2022): 543. http://dx.doi.org/10.3390/ijgi11110543.

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As the core of location-based social networks (LBSNs), the main task of next point-of-interest (POI) recommendation is to predict the next possible POI through the context information from users’ historical check-in trajectories. It is well known that spatial–temporal contextual information plays an important role in analyzing users check-in behaviors. Moreover, the information between POIs provides a non-trivial correlation for modeling users visiting preferences. Unfortunately, the impact of such correlation information and the spatio–temporal unequal interval information between POIs on user selection of next POI, is rarely considered. Therefore, we propose a spatio-temporal unequal interval correlation-aware self-attention network (STUIC-SAN) model for next POI recommendation. Specifically, we first use the linear regression method to obtain the spatio-temporal unequal interval correlation between any two POIs from users’ check-in sequences. Sequentially, we design a spatio-temporal unequal interval correlation-aware self-attention mechanism, which is able to comprehensively capture users’ personalized spatio-temporal unequal interval correlation preferences by incorporating multiple factors, including POIs information, spatio-temporal unequal interval correlation information between POIs, and the absolute positional information of corresponding POIs. On this basis, we perform next POI recommendation. Finally, we conduct comprehensive performance evaluation using large-scale real-world datasets from two popular location-based social networks, namely, Foursquare and Gowalla. Experimental results on two datasets indicate that the proposed STUIC-SAN outperformed the state-of-the-art next POI recommendation approaches regarding two commonly used evaluation metrics.

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32

Richardson, Alex D., Tibor Antal, Richard A. Blythe, and Linus J. Schumacher. "Learning spatio-temporal patterns with Neural Cellular Automata." PLOS Computational Biology 20, no. 4 (2024): e1011589. http://dx.doi.org/10.1371/journal.pcbi.1011589.

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Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and PDE trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give stationary emergent structures. We extend NCA to capture both transient and stable structures within the same system, as well as learning rules that capture the dynamics of Turing pattern formation in nonlinear Partial Differential Equations (PDEs). We demonstrate that NCA can generalise very well beyond their PDE training data, we show how to constrain NCA to respect given symmetries, and we explore the effects of associated hyperparameters on model performance and stability. Being able to learn arbitrary dynamics gives NCA great potential as a data driven modelling framework, especially for modelling biological pattern formation.

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33

G M, Basavaraj, and Ashok Kusagur. "Crowd Anomaly Detection Using Motion Based Spatio-Temporal Feature Analysis." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 3 (2017): 737. http://dx.doi.org/10.11591/ijeecs.v7.i3.pp737-747.

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Recently, the demand for surveillance system is increasing in real time application to enhance the security system. These surveillance systems are mainly used in crowded places such as shopping malls, sports stadium etc. In order to support enhance the security system, crowd behavior analysis has been proven a significant technique which is used for crowd monitoring, visual surveillance etc. For crowd behavior analysis, motion analysis is a crucial task which can be achieved with the help of trajectories and tracking of objects. Various approaches have been proposed for crowd behavior analysis which has limitation for densely crowded scenarios, a new object entering the scene etc. In this work, we propose a new approach for abnormal crowd behavior detection. Proposed approach is a motion based spatio-temporal feature analysis technique which is capable of obtaining trajectories of each detected object. We also present a technique to carry out the evaluation of individual object and group of objects by considering relational descriptors based on their environmental context. Finally, a classification is carried out for detection of abnormal or normal crowd behavior by following patch based process. In the results, we have reported that proposed model is able to achieve better performance when compared to existing techniques in terms of classification accuracy, true positive rate, and false positive rate.

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34

Cheng, J., J. Huang, and X. Zhang. "CASTLE: A CONTEXT-AWARE SPATIAL-TEMPORAL LOCATION EMBEDDING PRE-TRAINING MODEL FOR NEXT LOCATION PREDICTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W2-2022 (January 12, 2023): 15–21. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w2-2022-15-2023.

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Abstract. Next location prediction is helpful for service recommendation, public safety, intelligent transportation, and other location-based applications. Existing location prediction methods usually use sparse check-in trajectories and require massive historical data to capture complex spatial-temporal correlations. High spatial-temporal resolution trajectories have rich information. However, obtaining personal trajectories with long time series and high spatio-temporal resolution usually proves challenging. Herein, this paper proposes a two-stage Context-Aware Spatial-Temporal Location Embedding (CASTLE) model, a multi-modal pre-training model for sequence-to- sequence prediction tasks. The method is built in two steps. First, large-scale location datasets, which are sparse but easier to be acquired (i.e., check-in and anomalous navigation data), are used for pre-training location embedding to capture the multi-functional properties under different contexts. After that, the learned contextual embedding is used for downstream location prediction in small-scale but higher spatio-temporal resolution trajectory datasets. Specifically, the CASTLE model combines Bidirectional and Auto-Regressive Transformers to generate contextual embedding vectors rather than a fixed vector for each location. Furthermore, we introduce a location and time-aware encoder to reflect the spatial distances between locations and visit times. Experiments are conducted on two real trajectory datasets. The results show that the CASTLE model can pre-train beneficial location embedding and outperforms the model without pre-training by 4.6–7.1%. The proposed method is expected to improve the next location prediction accuracy without massive historical data, which will greatly drive the use of trajectory data.

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35

Lu, Hui, Albert Ali Salah, and Ronald Poppe. "TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3891–99. http://dx.doi.org/10.1609/aaai.v38i4.28181.

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A key challenge in continuous sign language recognition (CSLR) is to efficiently capture long-range spatial interactions over time from the video input. To address this challenge, we propose TCNet, a hybrid network that effectively models spatio-temporal information from Trajectories and Correlated regions. TCNet's trajectory module transforms frames into aligned trajectories composed of continuous visual tokens. This facilitates extracting region trajectory patterns. In addition, for a query token, self-attention is learned along the trajectory. As such, our network can also focus on fine-grained spatio-temporal patterns, such as finger movement, of a region in motion. TCNet's correlation module utilizes a novel dynamic attention mechanism that filters out irrelevant frame regions. Additionally, it assigns dynamic key-value tokens from correlated regions to each query. Both innovations significantly reduce the computation cost and memory. We perform experiments on four large-scale datasets: PHOENIX14, PHOENIX14-T, CSL, and CSL-Daily. Our results demonstrate that TCNet consistently achieves state-of-the-art performance. For example, we improve over the previous state-of-the-art by 1.5\% and 1.0\% word error rate on PHOENIX14 and PHOENIX14-T, respectively. Code is available at https://github.com/hotfinda/TCNet

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36

Tamilmani, Rajesh, and Emmanuel Stefanakis. "Semantically Enriched Simplification of Trajectories." Proceedings of the ICA 2 (July 10, 2019): 1–8. http://dx.doi.org/10.5194/ica-proc-2-128-2019.

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Abstract. Moving objects that are equipped with GPS devices generate huge volumes of spatio-temporal data. This spatial and temporal information is used in tracing the path travelled by the object, so called trajectory. It is often difficult to handle this massive data as it contains millions of raw data points. The number of points in a trajectory is reduced by trajectory simplification techniques. While most of the simplification algorithms use the distance offset as a criterion to eliminate the redundant points, temporal dimension in trajectories should also be considered in retaining the points which convey both the spatial and temporal characteristics of the trajectory. In addition to that the simplification process may result in losing the semantics associated with the intermediate points on the original trajectories. These intermediate points can contain attributes or characteristics depending on the application domain. For example, a trajectory of a moving vessel can contain information about distance travelled, bearing, and current speed. This paper involves implementing the Synchronized Euclidean Distance (SED) based simplification to consider the temporal dimension and building the Semantically Enriched Line simpliFication(SELF) data structure to preserve the semantic attributes associated to individual points on actual trajectories. The SED based simplification technique and the SELF data structure have been implemented in PostgreSQL 9.4 with PostGIS extension using PL/pgSQL to support dynamic lines. Extended experimental work has been carried out to better understand the impact of SED based simplification over conventional Douglas-Peucker algorithm to both synthetic and real trajectories. The efficiency of SELF structure in regard to semantic preservation has been tested at different levels of simplification.

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37

Demšar, Urška, and Kirsi Virrantaus. "Space–time density of trajectories: exploring spatio-temporal patterns in movement data." International Journal of Geographical Information Science 24, no. 10 (2010): 1527–42. http://dx.doi.org/10.1080/13658816.2010.511223.

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38

Arslan, Muhammad, Christophe Cruz, and Dominique Ginhac. "Semantic Enrichment of Spatio-temporal Trajectories for Worker Safety on Construction Sites." Procedia Computer Science 130 (2018): 271–78. http://dx.doi.org/10.1016/j.procs.2018.04.039.

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39

Cuenca-Jara, Jesús, Fernando Terroso-Sáenz, Mercedes Valdés-Vela, and Antonio F. Skarmeta. "Classification of spatio-temporal trajectories from Volunteer Geographic Information through fuzzy rules." Applied Soft Computing 86 (January 2020): 105916. http://dx.doi.org/10.1016/j.asoc.2019.105916.

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40

Arslan, Muhammad, Christophe Cruz, and Dominique Ginhac. "Semantic enrichment of spatio-temporal trajectories for worker safety on construction sites." Personal and Ubiquitous Computing 23, no. 5-6 (2019): 749–64. http://dx.doi.org/10.1007/s00779-018-01199-5.

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41

Hosseinpoor Milaghardan, Amin, Rahim Ali Abbaspour, and Christophe Claramunt. "A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity." Entropy 20, no. 7 (2018): 490. http://dx.doi.org/10.3390/e20070490.

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42

Nasiri, Afsaneh, Sanaz Azimi, and Rahim Ali Abbaspour. "Data Reduction of Spatio-temporal Trajectories using a Modified Online Compression Algorithm." Journal of Geospatial Information Technology 6, no. 3 (2018): 23–38. http://dx.doi.org/10.29252/jgit.6.3.23.

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43

Serrano, Ismael, Oscar Deniz, Gloria Bueno, Guillermo Garcia-Hernando, and Tae-Kyun Kim. "Spatio-temporal elastic cuboid trajectories for efficient fight recognition using Hough forests." Machine Vision and Applications 29, no. 2 (2017): 207–17. http://dx.doi.org/10.1007/s00138-017-0894-7.

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44

Li, Ye, Hongxiang Ren, and Haijiang Li. "PyVT: A toolkit for preprocessing and analysis of vessel spatio-temporal trajectories." SoftwareX 21 (February 2023): 101316. http://dx.doi.org/10.1016/j.softx.2023.101316.

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45

Chen, Ying, Guangyuan Li, Kun Zhou, and Caicong Wu. "Field–Road Operation Classification of Agricultural Machine GNSS Trajectories Using Spatio-Temporal Neural Network." Agronomy 13, no. 5 (2023): 1415. http://dx.doi.org/10.3390/agronomy13051415.

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The classification that distinguishes whether machines are driving on roads or working in fields based on their global navigation satellite system (GNSS) trajectories is essential for effective management of cross-regional agricultural machinery services in China. In this paper, a novel field–road classification method utilizing multiple deep neural networks (MultiDNN) is proposed to enhance the accuracy of field and road point classification. The MultiDNN model incorporates a bi-directional long short-term memory network (BiLSTM), a topology adaptive graph convolution network (TAG), and a self-attention network (ATT) to effectively extract spatio-temporal features for field–road classification. The BiLSTM is used to capture temporal relationships along the time axis of a trajectory, providing global contextual information for each point. Then, the TAG network is used to obtain the spatio-temporal relationships between adjacent points in a trajectory, offering local contextual information for each point. Finally, the ATT network assigns varying weights to features to emphasize important characteristics. The performance of the MultiDNN model was evaluated using a wheat harvesting trajectory dataset, and the results showed that it achieved a high degree of accuracy, up to 89.75%, outperforming the best baseline method (GCN) by 2.79%.

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46

Dritsas, Elias, Andreas Kanavos, Maria Trigka, Spyros Sioutas, and Athanasios Tsakalidis. "Storage Efficient Trajectory Clustering and k-NN for Robust Privacy Preserving Spatio-Temporal Databases." Algorithms 12, no. 12 (2019): 266. http://dx.doi.org/10.3390/a12120266.

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The need to store massive volumes of spatio-temporal data has become a difficult task as GPS capabilities and wireless communication technologies have become prevalent to modern mobile devices. As a result, massive trajectory data are produced, incurring expensive costs for storage, transmission, as well as query processing. A number of algorithms for compressing trajectory data have been proposed in order to overcome these difficulties. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. In the context of this research work, we focus on both the privacy preservation and storage problem of spatio-temporal databases. To alleviate this issue, we propose an efficient framework for trajectories representation, entitled DUST (DUal-based Spatio-temporal Trajectory), by which a raw trajectory is split into a number of linear sub-trajectories which are subjected to dual transformation that formulates the representatives of each linear component of initial trajectory; thus, the compressed trajectory achieves compression ratio equal to M : 1 . To our knowledge, we are the first to study and address k-NN queries on nonlinear moving object trajectories that are represented in dual dimensional space. Additionally, the proposed approach is expected to reinforce the privacy protection of such data. Specifically, even in case that an intruder has access to the dual points of trajectory data and try to reproduce the native points that fit a specific component of the initial trajectory, the identity of the mobile object will remain secure with high probability. In this way, the privacy of the k-anonymity method is reinforced. Through experiments on real spatial datasets, we evaluate the robustness of the new approach and compare it with the one studied in our previous work.

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47

Sato, Yuta, Yoko Sasaki, and Hiroshi Takemura. "STP4: spatio temporal path planning based on pedestrian trajectory prediction in dense crowds." PeerJ Computer Science 9 (October 30, 2023): e1641. http://dx.doi.org/10.7717/peerj-cs.1641.

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This article proposes a means of autonomous mobile robot navigation in dense crowds based on predicting pedestrians’ future trajectories. The method includes a pedestrian trajectory prediction for a running mobile robot and spatiotemporal path planning for when the path crosses with pedestrians. The predicted trajectories are converted into a time series of cost maps, and the robot achieves smooth navigation without dodging to the right or left in crowds; the path planner does not require a long-term prediction. The results of an evaluation implementing this method in a real robot in a science museum show that the trajectory prediction works. Moreover, the proposed planning’s arrival times is 26.4% faster than conventional 2D path planning’s arrival time in a simulation of navigation in a crowd of 50 people.

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48

ILG, WINFRIED, GÖKHAN H. BAKIR, JOHANNES MEZGER, and MARTIN A. GIESE. "ON THE REPRESENTATION, LEARNING AND TRANSFER OF SPATIO-TEMPORAL MOVEMENT CHARACTERISTICS." International Journal of Humanoid Robotics 01, no. 04 (2004): 613–36. http://dx.doi.org/10.1142/s0219843604000320.

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In this paper we present a learning-based approach for the modeling of complex movement sequences. Based on the method of Spatio-Temporal Morphable Models (STMMs) we derive a hierarchical algorithm that, in a first step, identifies automatically movement elements in movement sequences based on a coarse spatio-temporal description, and in a second step models these movement primitives by approximation through linear combinations of learned example movement trajectories. We describe the different steps of the algorithm and show how it can be applied for modeling and synthesis of complex sequences of human movements that contain movement elements with a variable style. The proposed method is demonstrated on different applications of movement representation relevant for imitation learning of movement styles in humanoid robotics.

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49

Xie, Jincan, Shuang Li, and Chunsheng Liu. "Traffic Agents Trajectory Prediction Based on Spatial–Temporal Interaction Attention." Sensors 23, no. 18 (2023): 7830. http://dx.doi.org/10.3390/s23187830.

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Trajectory prediction aims to predict the movement intention of traffic participants in the future based on the historical observation trajectories. For traffic scenarios, pedestrians, vehicles and other traffic participants have social interaction of surrounding traffic participants in both time and spatial dimensions. Most previous studies only use pooling methods to simulate the interaction process between participants and cannot fully capture the spatio-temporal dependence, possibly accumulating errors with the increase in prediction time. To overcome these problems, we propose the Spatial–Temporal Interaction Attention-based Trajectory Prediction Network (STIA-TPNet), which can effectively model the spatial–temporal interaction information. Based on trajectory feature extraction, the novel Spatial–Temporal Interaction Attention Module (STIA Module) is proposed to extract the interaction relationships between traffic participants, including temporal interaction attention, spatial interaction attention, and spatio-temporal attention fusion. By adaptive allocation of attention weights, temporal interaction attention is a temporal attention mechanism used to capture the movement pattern of each traffic participant in the scene, which can learn the importance of historical trajectories at different moments to future behaviors. Since the participants number in recent traffic scenes dynamically changes, the spatial interaction attention is designed to abstract the traffic participants in the scene into graph nodes, and abstract the social interaction between participants into graph edges. Coupling the temporal and spatial interaction attentions can adaptively model the temporal–spatial information and achieve accurate trajectory prediction. By performing experiments on the INTERACTION dataset and the UTP (Unmanned Aerial Vehicle-based Trajectory Prediction) dataset, the experimental results show that the proposed method significantly improves the accuracy of trajectory prediction and outperforms the representative methods in comparison.

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50

Junge, Wolfgang. "Spatio-temporal resolution of primary processes of photosynthesis." Faraday Discussions 177 (2015): 547–62. http://dx.doi.org/10.1039/c5fd90015h.

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Technical progress in laser-sources and detectors has allowed the temporal and spatial resolution of chemical reactions down to femtoseconds and Å-units. In photon-excitable systems the key to chemical kinetics, trajectories across the vibrational saddle landscape, are experimentally accessible. Simple and thus well-defined chemical compounds are preferred objects for calibrating new methodologies and carving out paradigms of chemical dynamics, as shown in several contributions to thisFaraday Discussion. Aerobic life on earth is powered by solar energy, which is captured by microorganisms and plants. Oxygenic photosynthesis relies on a three billion year old molecular machinery which is as well defined as simpler chemical constructs. It has been analysed to a very high precision. The transfer of excitation between pigments in antennae proteins, of electrons between redox-cofactors in reaction centres, and the oxidation of water by a Mn4Ca-cluster are solid state reactions. ATP, the general energy currency of the cell, is synthesized by a most agile, rotary molecular machine. While the efficiency of photosynthesis competes well with photovoltaics at the time scale of nanoseconds, it is lower by an order of magnitude for crops and again lower for bio-fuels. The enormous energy demand of mankind calls for engineered (bio-mimetic or bio-inspired) solar-electric and solar-fuel devices.

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