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Artículos de revistas sobre el tema "Spatio-temporal trajectories"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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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

Madanayake, Adikarige Randil Sanjeewa, Kyungmi Lee, and Ickjai Lee. "Mining contacts from spatio-temporal trajectories." AI Open 5 (2024): 197–207. http://dx.doi.org/10.1016/j.aiopen.2024.10.002.

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3

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 m

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4

Zhou, Yan, Yunhan Zhang, Fangfang Zhang, Yeting Zhang, and Xiaodi Wang. "Trajectory Compression with Spatio-Temporal Semantic Constraints." ISPRS International Journal of Geo-Information 13, no. 6 (2024): 212. http://dx.doi.org/10.3390/ijgi13060212.

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Most trajectory compression methods primarily focus on geometric similarity between compressed and original trajectories, lacking explainability of compression results due to ignoring semantic information. This paper proposes a spatio-temporal semantic constrained trajectory compression method. It constructs a new trajectory distance measurement model integrating both semantic and spatio-temporal features. This model quantifies semantic features using information entropy and measures spatio-temporal features with synchronous Euclidean distance. The compression principle is to retain feature po

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5

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 descri

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6

Yang, Yuqi, Xiaoqing Zuo, Kang Zhao, and Yongfa Li. "Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data." ISPRS International Journal of Geo-Information 13, no. 6 (2024): 197. http://dx.doi.org/10.3390/ijgi13060197.

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The presence of abundant spatio-temporal information based on the location of mobile objects in publicly accessible GPS mobile devices makes it crucial to collect, analyze, and mine such information. Therefore, it is necessary to index a large volume of trajectory data to facilitate efficient trajectory retrieval and access. It is difficult for existing indexing methods that primarily rely on data-driven indexing structures (such as R-Tree) or space-driven indexing structures (such as Quadtree) to support efficient analysis and computation of data based on spatio-temporal range queries as a se

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7

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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8

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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9

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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10

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 ge

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11

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 pat

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12

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 re

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13

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 w

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14

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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15

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 tempo

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16

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 attrac

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17

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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<p><strong>Abstract.</strong> 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 p

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18

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

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19

Cui, Zhoujuan, Wenshuo Peng, Yaqiang Zhang, Yiping Duan, and Xiaoming Tao. "Spatio-Temporal-Interaction Graph Neural Networks for Multi-Agent Trajectory Prediction." Journal of Physics: Conference Series 2833, no. 1 (2024): 012010. http://dx.doi.org/10.1088/1742-6596/2833/1/012010.

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Abstract For intelligent transportation systems, accurately forecasting the future trajectories of multiple agents is pivotal. Considering the increased diversity of agents within a scene, in order to capture and model the variations in their appearance, motion status, behavioral patterns, and interrelationships, we propose a simple yet effective framework based on Spatio-Temporal-Interaction Graph Neural Networks. Specifically, a Multi-Class Agent Encoder is meticulously tailored to the specific class of each agent to distill pertinent information from their motion attributes and historical t

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20

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 t

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21

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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22

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 t

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23

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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24

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

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25

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. Specifica

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26

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 b

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27

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 traje

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28

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

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29

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 mod

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30

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 use

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31

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. Thi

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32

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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33

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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34

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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35

Meng, Dexu, Guangzhe Zhao, and Feihu Yan. "Social-STGMLP: A Social Spatio-Temporal Graph Multi-Layer Perceptron for Pedestrian Trajectory Prediction." Information 15, no. 6 (2024): 341. http://dx.doi.org/10.3390/info15060341.

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As autonomous driving technology advances, the imperative of ensuring pedestrian traffic safety becomes increasingly prominent within the design framework of autonomous driving systems. Pedestrian trajectory prediction stands out as a pivotal technology aiming to address this challenge by striving to precisely forecast pedestrians’ future trajectories, thereby enabling autonomous driving systems to execute timely and accurate decisions. However, the prevailing state-of-the-art models often rely on intricate structures and a substantial number of parameters, posing challenges in meeting the imp

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36

Guo, Jianhua, Zhihao Xie, Ming Liu, et al. "Spatio-Temporal Joint Optimization-Based Trajectory Planning Method for Autonomous Vehicles in Complex Urban Environments." Sensors 24, no. 14 (2024): 4685. http://dx.doi.org/10.3390/s24144685.

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Providing safe, smooth, and efficient trajectories for autonomous vehicles has long been a question of great interest in the field of autopiloting. In dynamic and ever-changing urban environments, safe and efficient trajectory planning is fundamental to achieving autonomous driving. Nevertheless, the complexity of environments with multiple constraints poses challenges for trajectory planning. It is possible that behavior planners may not successfully obtain collision-free trajectories in complex urban environments. Herein, this paper introduces spatio–temporal joint optimization-based traject

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37

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 base

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38

Basavaraj, G. M., and Kusagur Ashok. "Crowd Anomaly Detection Using Motion Based Spatio-Temporal Feature Analysis." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (2017): 737–47. https://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

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39

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 Equatio

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40

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 Embe

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41

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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<p>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

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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-gra

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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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<p><strong>Abstract.</strong> 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

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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 sel

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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

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Zhang, Hao, Wei Chen, Xingyu Zhao, Jianpeng Qi, Guiyuan Jiang, and Yanwei Yu. "Scalable Trajectory-User Linking with Dual-Stream Representation Networks." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 13224–32. https://doi.org/10.1609/aaai.v39i12.33443.

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Trajectory-user linking (TUL) aims to match anonymous trajectories to the most likely users who generated them, offering benefits for a wide range of real-world spatio-temporal applications. However, existing TUL methods are limited by high model complexity and poor learning of the effective representations of trajectories, rendering them ineffective in handling large-scale user trajectory data.In this work, we propose a novel Scalable Trajectory-User Linking with dual-stream representation networks for large-scale TUL problem, named ScaleTUL Specifically, ScaleTUL generates two views using te

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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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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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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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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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