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Journal articles on the topic 'Trajectory Datasets'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Zhu, Hong, Yunkai Yu, and Meiyi Xie. "Releasing Differential Private Trajectory Datasets Without Revealing Trajectory Correlations." Security and Communication Networks 2022 (June 29, 2022): 1–19. http://dx.doi.org/10.1155/2022/2590648.

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With the prevailing use of smartphones and location-based services, vast amounts of trajectory data are collected and used for many applications. When trajectory data are sent to a third-party research institute for analytical applications, the privacy of users would be severely disclosed. For example, the relationship between users will be revealed from the correlation between trajectories. In this paper, we propose a method for releasing trajectory datasets without revealing correlation between trajectories, called RDPT. In RDPT, we first quantify the trajectory correlation and convert the p

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Park, Daehee, Jaewoo Jeong, and Kuk-Jin Yoon. "Improving Transferability for Cross-Domain Trajectory Prediction via Neural Stochastic Differential Equation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 10145–54. http://dx.doi.org/10.1609/aaai.v38i9.28879.

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Multi-agent trajectory prediction is crucial for various practical applications, spurring the construction of many large-scale trajectory datasets, including vehicles and pedestrians. However, discrepancies exist among datasets due to external factors and data acquisition strategies. External factors include geographical differences and driving styles, while data acquisition strategies include data acquisition rate, history/prediction length, and detector/tracker error. Consequently, the proficient performance of models trained on large-scale datasets has limited transferability on other small

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BARBIE, Thibault, Takaki NISHIO, and Takeshi NISHIDA. "Trajectory Prediction without Requiring Online Datasets." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2018 (2018): 2A2—E16. http://dx.doi.org/10.1299/jsmermd.2018.2a2-e16.

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Santiago-Chaparro, Kelvin R., and David A. Noyce. "Expanding the Capabilities of Radar-Based Vehicle Detection Systems: Noise Characterization and Removal Procedures." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (2019): 150–60. http://dx.doi.org/10.1177/0361198119852607.

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The capabilities of radar-based vehicle detection (RVD) systems used at signalized intersections for stop bar and advanced detection are arguably underutilized. Underutilization happens because RVD systems can monitor the position and speed (i.e., trajectory) of multiple vehicles at the same time but these trajectories are only used to emulate the behavior of legacy detection systems such as inductive loop detectors. When full vehicle trajectories tracked by an RVD system are collected, detailed traffic operations and safety performance measures can be calculated for signalized intersections.

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Silber, Israel, Jennifer M. Comstock, Michael R. Kieburtz, and Lynn M. Russell. "ARMTRAJ: a set of multipurpose trajectory datasets augmenting the Atmospheric Radiation Measurement (ARM) user facility measurements." Earth System Science Data 17, no. 1 (2025): 29–42. https://doi.org/10.5194/essd-17-29-2025.

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Abstract. Ground-based instruments offer unique capabilities such as detailed atmospheric, thermodynamic, cloud, and aerosol profiling at a high temporal sampling rate. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility provides comprehensive datasets from key locations around the globe, facilitating long-term characterization and process-level understanding of clouds, aerosol, and aerosol–cloud interactions. However, as with other ground-based datasets, the fixed (Eulerian) nature of these measurements often introduces a knowledge gap in relating those observa

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Lim, Hong Seo, and Peng Qiu. "Quantifying the clusterness and trajectoriness of single-cell RNA-seq data." PLOS Computational Biology 20, no. 2 (2024): e1011866. http://dx.doi.org/10.1371/journal.pcbi.1011866.

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Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the “clusterness” and “trajectoriness” of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introdu

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Xiong, Wen, Xiaoxuan Wang, and Hao Li. "Efficient Large-Scale GPS Trajectory Compression on Spark: A Pipeline-Based Approach." Electronics 12, no. 17 (2023): 3569. http://dx.doi.org/10.3390/electronics12173569.

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Every day, hundreds of thousands of vehicles, including buses, taxis, and ride-hailing cars, continuously generate GPS positioning records. Simultaneously, the traffic big data platform of urban transportation systems has already collected a large amount of GPS trajectory datasets. These incremental and historical GPS datasets require more and more storage space, placing unprecedented cost pressure on the big data platform. Therefore, it is imperative to efficiently compress these large-scale GPS trajectory datasets, saving storage cost and subsequent computing cost. However, a set of classica

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Zong, Ruixue, Ying Wang, Juan Ding, and Weiwen Deng. "Statistical Risk and Performance Analyses on Naturalistic Driving Trajectory Datasets for Traffic Modeling." World Electric Vehicle Journal 15, no. 3 (2024): 77. http://dx.doi.org/10.3390/wevj15030077.

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The development of autonomous driving technology has made simulation testing one of the most important tools for evaluating system performance. However, there is a lack of systematic methods for analyzing and assessing naturalistic driving trajectory datasets. Specifically, there is a lack of comprehensive analyses on data diversity and balance in machine learning-oriented research. This study presents a comprehensive assessment of existing highway scenario datasets in the context of traffic modeling in autonomous driving simulation tests. In order to clarify the level of traffic risk, we desi

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Lan, Doi Thi, and Seokhoon Yoon. "Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement." Sensors 23, no. 6 (2023): 3318. http://dx.doi.org/10.3390/s23063318.

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Human movement anomalies in indoor spaces commonly involve urgent situations, such as security threats, accidents, and fires. This paper proposes a two-phase framework for detecting indoor human trajectory anomalies based on density-based spatial clustering of applications with noise (DBSCAN). The first phase of the framework groups datasets into clusters. In the second phase, the abnormality of a new trajectory is checked. A new metric called the longest common sub-sequence using indoor walking distance and semantic label (LCSS_IS) is proposed to calculate the similarity between trajectories,

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Kim, Jong Wook, and Beakcheol Jang. "Effectively computing transition patterns with privacy-preserved trajectory datasets." PLOS ONE 17, no. 12 (2022): e0278744. http://dx.doi.org/10.1371/journal.pone.0278744.

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Recent advances in positioning techniques, along with the widespread use of mobile devices, make it easier to monitor and collect user trajectory information during their daily activities. An ever-growing abundance of data about trajectories of individual users paves the way for various applications that utilize user mobility information. One of the most common analysis tasks in these new applications is to extract the sequential transition patterns between two consecutive timestamps from a collection of trajectories. Such patterns have been widely exploited in diverse applications to predict

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Wu, Laiyun, Samiul Hasan, Younshik Chung, and Jee Eun Kang. "Understanding the Heterogeneity of Human Mobility Patterns: User Characteristics and Modal Preferences." Sustainability 13, no. 24 (2021): 13921. http://dx.doi.org/10.3390/su132413921.

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Characterizing individual mobility is critical to understand urban dynamics and to develop high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal mobility patterns. However, due to the limitations of the underlying datasets, these studies could not investigate how mobility patterns differ over user characteristics among demographic groups. In this study, we analyzed a large-scale Automatic Fare Collection (AFC) dataset of the transit system of Seoul, South Korea and investigated how mobility patterns vary over user characteristics

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Lucchese, Claudio, Michail Vlachos, Deepak Rajan, and Philip S. Yu. "Rights protection of trajectory datasets with nearest-neighbor preservation." VLDB Journal 19, no. 4 (2010): 531–56. http://dx.doi.org/10.1007/s00778-010-0178-6.

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Lin, Yingxin, Shila Ghazanfar, Kevin Y. X. Wang, et al. "scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets." Proceedings of the National Academy of Sciences 116, no. 20 (2019): 9775–84. http://dx.doi.org/10.1073/pnas.1820006116.

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Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery

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Jiang, Yuzhou, Emre Yilmaz, and Erman Ayday. "Robust Fingerprint of Privacy-Preserving Location Trajectories." Proceedings on Privacy Enhancing Technologies 2023, no. 4 (2023): 5–20. http://dx.doi.org/10.56553/popets-2023-0095.

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Location-based services have brought significant convenience to people in their daily lives, and trajectory data are also in high demand. However, directly releasing those data raises privacy and liability (e.g., due to unauthorized distribution of such datasets) concerns since location data contain users' sensitive information, e.g., regular moving patterns and favorite spots. To address this, we propose a novel fingerprinting scheme that simultaneously identifies unauthorized redistribution of location trajectory datasets and provides differential privacy guarantees for shared data. Observin

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Ma, Yuexin, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, and Dinesh Manocha. "TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6120–27. http://dx.doi.org/10.1609/aaai.v33i01.33016120.

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To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances’

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Musleh, Mashaal, and Mohamed F. Mokbel. "Kamel: A Scalable BERT-Based System for Trajectory Imputation." Proceedings of the VLDB Endowment 17, no. 3 (2023): 525–38. http://dx.doi.org/10.14778/3632093.3632113.

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Numerous important applications rely on detailed trajectory data. Yet, unfortunately, trajectory datasets are typically sparse with large spatial and temporal gaps between each two points, which is a major hurdle for their accuracy. This paper presents Kamel; a scalable trajectory imputation system that inserts additional realistic trajectory points, boosting the accuracy of trajectory applications. Kamel maps the trajectory imputation problem to finding the missing word problem; a classical problem in the natural language processing (NLP) community. This allows employing the widely used BERT

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Lu, Bingxian, Di Wu, Zhenquan Qin, and Lei Wang. "Privacy-Preserving Indoor Trajectory Matching with IoT Devices." Sensors 23, no. 8 (2023): 4029. http://dx.doi.org/10.3390/s23084029.

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With the rapid development of the Internet of Things (IoT) technology, Wi-Fi signals have been widely used for trajectory signal acquisition. Indoor trajectory matching aims to achieve the monitoring of the encounters between people and trajectory analysis in indoor environments. Due to constraints ofn the computation abilities IoT devices, the computation of indoor trajectory matching requires the assistance of a cloud platform, which brings up privacy concerns. Therefore, this paper proposes a trajectory-matching calculation method that supports ciphertext operations. Hash algorithms and hom

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18

Huang, Ping, and Jinliang Lu. "Learning Trajectory Patterns via Canonical Correlation Analysis." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 2 (2021): 1–17. http://dx.doi.org/10.4018/ijcini.20210401.oa1.

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A substantial body of research has been devoted to the analysis of motion trajectories. Usually, a motion trajectory consists of a set of coordinates, which is called a raw trajectory. In this paper, the authors first use vectors for some artificially constructed global features, such as the mean discrete curvature and standard deviation of acceleration, to represent the raw trajectory data, and then apply a multiset canonical correlation analysis method to extract latent features from the artificially constructed features. The performance of the latent features is then measured by evaluating

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19

Zhou, Tian, Radhika Ravi, Yi-Chun Lin, Raja Manish, Songlin Fei, and Ayman Habib. "In Situ Calibration and Trajectory Enhancement of UAV and Backpack LiDAR Systems for Fine-Resolution Forest Inventory." Remote Sensing 15, no. 11 (2023): 2799. http://dx.doi.org/10.3390/rs15112799.

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Forest inventory has been relying on labor-intensive manual measurements. Using remote sensing modalities for forest inventory has gained increasing attention in the last few decades. However, tools for deriving accurate tree-level metrics are limited. This paper investigates the feasibility of using LiDAR units onboard uncrewed aerial vehicle (UAV) and Backpack mobile mapping systems (MMSs) equipped with an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) to provide high-quality point clouds for accurate, fine-resolution forest inventory. To improve the qual

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Castanheiro, L. F., A. M. G. Tommaselli, M. V. Machado, G. H. Santos, I. S. Norberto, and T. T. Reis. "THE USE OF A WIDE FOV LASER SCANNING SYSTEM AND A SLAM ALGORITHM FOR MOBILE APPLICATIONS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2022 (May 30, 2022): 181–87. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2022-181-2022.

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Abstract. This paper presents the assessment of a wide-angle laser scanner and a simultaneous localisation and mapping (SLAM) algorithm to estimate the trajectory and generate a 3D map of the environment. A backpack platform composed of an OS0-128 Ouster (FoV 90° × 360°) laser scanner was used to acquire laser data in an area with urban and forest features. Web SLAM, an online SLAM algorithm implemented by Ouster, Inc., was used to estimate the trajectory and generate a 3D map in a local reference system. Then, the 3D point clouds were transformed into the ground coordinate system with a rigid

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Ding, Jiaman, Yunpeng Li, Ling Li, and Lianyin Jia. "Prefix-Pruning-Based Distributed Frequent Trajectory Pattern Mining Algorithm." Mathematical Problems in Engineering 2022 (May 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/3838147.

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An important problem to be solved in smart city construction is how to improve the efficiency of mining frequent patterns that can be used for location prediction and location-based services of massive trajectory datasets. Owing to uncertain personal trajectory and non-explicit trajectory items, the existing sequence mining algorithms cannot be used directly. To solve this problem, this study proposes a distributed trajectory frequent pattern mining algorithm (SparkTraj) based on prefix pruning. First, a grouping and partitioning technique is used to abstract the original trajectory data and c

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Sauer, Franz, Hongfeng Yu, and Kwan-Liu Ma. "Trajectory-Based Flow Feature Tracking in Joint Particle/Volume Datasets." IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (2014): 2565–74. http://dx.doi.org/10.1109/tvcg.2014.2346423.

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Arfa, Reza, Rubiyah Yusof, and Parvaneh Shabanzadeh. "Novel trajectory clustering method based on distance dependent Chinese restaurant process." PeerJ Computer Science 5 (August 12, 2019): e206. http://dx.doi.org/10.7717/peerj-cs.206.

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Trajectory clustering and path modelling are two core tasks in intelligent transport systems with a wide range of applications, from modeling drivers’ behavior to traffic monitoring of road intersections. Traditional trajectory analysis considers them as separate tasks, where the system first clusters the trajectories into a known number of clusters and then the path taken in each cluster is modelled. However, such a hierarchy does not allow the knowledge of the path model to be used to improve the performance of trajectory clustering. Based on the distance dependent Chinese restaurant process

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Lu, Lihui, Qizhong Li, Jiyuan Liu, and Miaohua Huang. "Combining Domain Knowledge and Deep Learning Methods for Vehicle Trajectory Prediction." Journal of Physics: Conference Series 2303, no. 1 (2022): 012034. http://dx.doi.org/10.1088/1742-6596/2303/1/012034.

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Abstract Predicting the future trajectory of surrounding agents is especially crucial for autonomous vehicles applied in dense traffic streams. Majority of the approaches presently implemented for vehicle trajectory prediction can be generally classified into domain knowledge-driven method and deep learning approach. Although domain priori knowledge such as traffic rules implementing in knowledge-driven method has realistic output, the interactive performance with other traffic agents is constrained. Conversely, data-driven approach can acquire superior interactive performance by training the

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Konak, Orhan, Pit Wegner, and Bert Arnrich. "IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition." Sensors 20, no. 24 (2020): 7179. http://dx.doi.org/10.3390/s20247179.

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Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this w

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Ma, Tinghuai, and Fagen Song. "A Trajectory Privacy Protection Method Based on Random Sampling Differential Privacy." ISPRS International Journal of Geo-Information 10, no. 7 (2021): 454. http://dx.doi.org/10.3390/ijgi10070454.

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With the popularity of location-aware devices (e.g., smart phones), a large number of trajectory data were collected. The trajectory dataset can be used in many fields including traffic monitoring, market analysis, city management, etc. The collection and release of trajectory data will raise serious privacy concerns for users. If users’ privacy is not protected enough, they will refuse to share their trajectory data. In this paper, a new trajectory privacy protection method based on random sampling differential privacy (TPRSDP), which can provide more security protection, is proposed. Compare

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Mirvahabi, S. S., R. Ali Abbaspour, and C. Claramunt. "A FLEXIBLE TRAJECTORY COMPRESSION ALGORITHM FOR MULTI-MODAL TRANSPORTATION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 14, 2023): 501–8. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-501-2023.

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Abstract. Continuous progress in navigation, sensor-based, and GPS technologies have made smart devices essential to our daily lives and many location-based applications. However, the trajectory datasets generated by these applications require the management of large data volumes while preserving their main properties and semantics. One of the most popular methods for compressing trajectory data offline is the Douglas–Peucker (DP) algorithm, but its principles should be applied to a diverse range of contexts when considering real-time trajectory data. This paper introduces a Flexible Douglas-P

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Ni, Qingjian, Wenqiang Peng, Yuntian Zhu, and Ruotian Ye. "A Novel Trajectory Feature-Boosting Network for Trajectory Prediction." Entropy 25, no. 7 (2023): 1100. http://dx.doi.org/10.3390/e25071100.

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Trajectory prediction is an essential task in many applications, including autonomous driving, robotics, and surveillance systems. In this paper, we propose a novel trajectory prediction network, called TFBNet (trajectory feature-boosting network), that utilizes trajectory feature boosting to enhance prediction accuracy. TFBNet operates by mapping the original trajectory data to a high-dimensional space, analyzing the change rules of the trajectory in this space, and finally aggregating the trajectory goals to generate the final trajectory. Our approach presents a new perspective on trajectory

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Yang, Zhao, Rong Tang, Jie Bao, Jiahuan Lu, and Zhijie Zhang. "A Real-Time Trajectory Prediction Method of Small-Scale Quadrotors Based on GPS Data and Neural Network." Sensors 20, no. 24 (2020): 7061. http://dx.doi.org/10.3390/s20247061.

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This paper proposes a real-time trajectory prediction method for quadrotors based on a bidirectional gated recurrent unit model. Historical trajectory data of ten types of quadrotors were obtained. The bidirectional gated recurrent units were constructed and utilized to learn the historic data. The prediction results were compared with the traditional gated recurrent unit method to test its prediction performance. The efficiency of the proposed algorithm was investigated by comparing the training loss and training time. The results over the testing datasets showed that the proposed model produ

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Qi, Mengjun, Zhongyuan Wang, Zheng He, and Zhenfeng Shao. "User Identification across Asynchronous Mobility Trajectories." Sensors 19, no. 9 (2019): 2102. http://dx.doi.org/10.3390/s19092102.

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With the popularity of location-based services and applications, a large amount of mobility data has been generated. Identification through mobile trajectory information, especially asynchronous trajectory data has raised great concerns in social security prevention and control. This paper advocates an identification resolution method based on the most frequently distributed TOP-N (the most frequently distributed N regions regarding user trajectories) regions regarding user trajectories. This method first finds TOP-N regions whose trajectory points are most frequently distributed to reduce the

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Zhu, Junhao, Tao Wang, Danlei Hu, et al. "T-Assess: An Efficient Data Quality Assessment System Tailored for Trajectory Data." Proceedings of the VLDB Endowment 18, no. 3 (2024): 666–74. https://doi.org/10.14778/3712221.3712233.

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With the widespread use of GPS-enabled devices and services, trajectory data fuels services in a variety of fields, such as transportation and smart cities. However, trajectory data often contains errors stemming from inaccurate GPS measurements, low sampling rates, and transmission interruptions, yielding low-quality trajectory data with negative effects on downstream services. Therefore, a crucial yet tedious endeavor is to assess the quality of trajectory data, serving as a guide for subsequent data cleaning and analyses. Despite some studies addressing general-purpose data quality assessme

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Zhang, Zikai. "Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction." Sensors 24, no. 23 (2024): 7538. http://dx.doi.org/10.3390/s24237538.

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This paper analyzes the rounD dataset to advance motion forecasting algorithms for autonomous vehicles navigating complex roundabout environments. We develop a trajectory prediction framework inspired by Gated Recurrent Unit (GRU) networks and graph-based modules to effectively model vehicle interactions. Our primary objective is to evaluate the generalizability of the proposed model across diverse training and testing datasets. Through extensive experiments, we investigate how varying data distributions—such as different road configurations and recording times—impact the model’s prediction ac

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Wu, Yuegao, Wanneng Yv, Guangmiao Zeng, Yifan Shang, and Weiqiang Liao. "GL-STGCNN: Enhancing Multi-Ship Trajectory Prediction with MPC Correction." Journal of Marine Science and Engineering 12, no. 6 (2024): 882. http://dx.doi.org/10.3390/jmse12060882.

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In addressing the challenges of trajectory prediction in multi-ship interaction scenarios and aiming to improve the accuracy of multi-ship trajectory prediction, this paper proposes a multi-ship trajectory prediction model, GL-STGCNN. The GL-STGCNN model employs a ship interaction adjacency matrix extraction module to obtain a more reasonable ship interaction adjacency matrix. Additionally, after obtaining the distribution of predicted trajectories using the model, a model predictive control trajectory correction method is introduced to enhance the accuracy and reasonability of the predicted t

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Liu, Liang Xu, Jia Tao Song, Bo Guan, Zhao Xiao Wu, and Ke Jia He. "Tra-DBScan: A Algorithm of Clustering Trajectories." Applied Mechanics and Materials 121-126 (October 2011): 4875–79. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4875.

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Accompany with fast development of location technology, more and more trajectories datasets are collected on the real applications. So it is something of value in the theory and applied research to mine the clusters from these datasets. In this paper, a trajectory clustering algorithm, called Density-Based Spatial Clustering of Application with noise (Tra-DBSCAN for short), based on DBSCAN that is a classic clustering algorithm. In this framework, each trajectory firstly partitions into sub-trajectories as clustering object, and then line hausdorff distance is used to measure the distance betw

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Fang, Meichen, Gennady Gorin, and Lior Pachter. "Trajectory inference from single-cell genomics data with a process time model." PLOS Computational Biology 21, no. 1 (2025): e1012752. https://doi.org/10.1371/journal.pcbi.1012752.

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Single-cell transcriptomics experiments provide gene expression snapshots of heterogeneous cell populations across cell states. These snapshots have been used to infer trajectories and dynamic information even without intensive, time-series data by ordering cells according to gene expression similarity. However, while single-cell snapshots sometimes offer valuable insights into dynamic processes, current methods for ordering cells are limited by descriptive notions of “pseudotime” that lack intrinsic physical meaning. Instead of pseudotime, we propose inference of “process time” via a principl

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Wu, Xiuen, Sien Li, Tao Wang, Ge Xu, and George Papageorgiou. "Learning a Memory-Enhanced Multi-Stage Goal-Driven Network for Egocentric Trajectory Prediction." Biomimetics 9, no. 8 (2024): 462. http://dx.doi.org/10.3390/biomimetics9080462.

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We propose a memory-enhanced multi-stage goal-driven network (ME-MGNet) for egocentric trajectory prediction in dynamic scenes. Our key idea is to build a scene layout memory inspired by human perception in order to transfer knowledge from prior experiences to the current scenario in a top-down manner. Specifically, given a test scene, we first perform scene-level matching based on our scene layout memory to retrieve trajectories from visually similar scenes in the training data. This is followed by trajectory-level matching and memory filtering to obtain a set of goal features. In addition, a

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LIU, Lihua. "A dataset of ship target tracking and trajectory fusion in maritime surveillance." China Scientific Data 9, no. 1 (2024): 1–5. http://dx.doi.org/10.11922/11-6035.csd.2023.0149.zh.

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Real-time trajectory association and trajectory fusion in maritime surveillance pose great challenges and remain hot issues in security, regional situation monitoring, and long-range precision strikes for both military and civilian applications. High-quality datasets play a pivotal role in advancing research in target tracking and fusion technologies within this domain. This paper addresses the data requirements for technological research in target tracking and fusion, as well as the limitations of currently available datasets, including data scarcity, inadequate scene design specificity, unif

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Chang, Zhihao, Linzhu Yu, Huan Li, Sai Wu, Gang Chen, and Dongxiang Zhang. "Revisiting CNNs for Trajectory Similarity Learning." Proceedings of the VLDB Endowment 18, no. 4 (2024): 1013–21. https://doi.org/10.14778/3717755.3717762.

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Similarity search is a fundamental but expensive operator in querying trajectory data, due to its quadratic complexity of distance computation. To mitigate the computational burden for long trajectories, neural networks have been widely employed for similarity learning and each trajectory is encoded as a high-dimensional vector for similarity search with linear complexity. Given the sequential nature of trajectory data, previous efforts have been primarily devoted to the utilization of RNNs or Transformers. In this paper, we argue that the common practice of treating trajectory as sequential d

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Schrader, Maxwell, Alexander Hainen, and Joshua Bittle. "Extracting Vehicle Trajectories from Partially Overlapping Roadside Radar." Sensors 24, no. 14 (2024): 4640. http://dx.doi.org/10.3390/s24144640.

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This work presents a methodology for extracting vehicle trajectories from six partially-overlapping roadside radars through a signalized corridor. The methodology incorporates radar calibration, transformation to the Frenet space, Kalman filtering, short-term prediction, lane-classification, trajectory association, and a covariance intersection-based approach to track fusion. The resulting dataset contains 79,000 fused radar trajectories over a 26-h period, capturing diverse driving scenarios including signalized intersections, merging behavior, and a wide range of speeds. Compared to popular

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Lv, Pei, Hui Wei, Tianxin Gu, et al. "Trajectory distributions: A new description of movement for trajectory prediction." Computational Visual Media 8, no. 2 (2021): 213–24. http://dx.doi.org/10.1007/s41095-021-0236-6.

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AbstractTrajectory prediction is a fundamental and challenging task for numerous applications, such as autonomous driving and intelligent robots. Current works typically treat pedestrian trajectories as a series of 2D point coordinates. However, in real scenarios, the trajectory often exhibits randomness, and has its own probability distribution. Inspired by this observation and other movement characteristics of pedestrians, we propose a simple and intuitive movement description called a trajectory distribution, which maps the coordinates of the pedestrian trajectory to a 2D Gaussian distribut

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Peng, Fei, Li Zheng, Zhu Duan, and Yu Xia. "Multi-Objective Multi-Learner Robot Trajectory Prediction Method for IoT Mobile Robot Systems." Electronics 11, no. 13 (2022): 2094. http://dx.doi.org/10.3390/electronics11132094.

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Robot trajectory prediction is an essential part of building digital twin systems and ensuring the high-performance navigation of IoT mobile robots. In the study, a novel two-stage multi-objective multi-learner model is proposed for robot trajectory prediction. Five machine learning models are adopted as base learners, including autoregressive moving average, multi-layer perceptron, Elman neural network, deep echo state network, and long short-term memory. A non-dominated sorting genetic algorithm III is applied to automatically combine these base learners, generating an accurate and robust en

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Chen, Wen-Hui, Jiann-Cherng Wu, Yury Davydov, Wei-Chen Yeh, and Yu-Chen Lin. "Impact of Perception Errors in Vision-Based Detection and Tracking Pipelines on Pedestrian Trajectory Prediction in Autonomous Driving Systems." Sensors 24, no. 15 (2024): 5066. http://dx.doi.org/10.3390/s24155066.

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Pedestrian trajectory prediction is crucial for developing collision avoidance algorithms in autonomous driving systems, aiming to predict the future movement of the detected pedestrians based on their past trajectories. The traditional methods for pedestrian trajectory prediction involve a sequence of tasks, including detection and tracking to gather the historical movement of the observed pedestrians. Consequently, the accuracy of trajectory prediction heavily relies on the accuracy of the detection and tracking models, making it susceptible to their performance. The prior research in trajec

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Lin, Lin, Jie Zhang, Xu Gao, Jiancheng Shi, Cheng Chen, and Nantian Huang. "Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet." PLOS ONE 18, no. 2 (2023): e0281482. http://dx.doi.org/10.1371/journal.pone.0281482.

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In power fingerprint identification, feature information is insufficient when using a single feature to identify equipment, and small load data of specific customers, difficult to meet the refined equipment classification needs. A power fingerprint identification based on the improved voltage-current(V-I) trajectory with color encoding and transferred CBAM-ResNet34 is proposed. First, the current, instantaneous power, and trajectory momentum information are added to the original V-I trajectory image using color coding to obtain a color V-I trajectory image. Then, the ResNet34 model was pre-tra

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Lebedev, Evgenie, Denis Uchaev, and Dmitry Uchaev. "Stability assessment and performance comparison of VSLAM frameworks on open indoor datasets." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W5-2024 (December 16, 2024): 101–7. https://doi.org/10.5194/isprs-archives-xlviii-2-w5-2024-101-2024.

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Abstract. This article examines the main directions of development of modern visual navigation technologies and highlights achievements and problems in this area. The article also considers popular VSLAM (Visual Simultaneous Localization and Mapping) frameworks and their main characteristics. Additionally, several indoor datasets are reviewed to highlight the importance of different testing environments when evaluating VSLAM frameworks. In the experimental part of the work, we compared three prominent VSLAM frameworks: ORB-SLAM 3, Basalt, and OpenVSLAM. For experimental study, 20 image sequenc

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Zhou, T., R. Manish, S. Fei, and A. Habib. "IN-SITU CALIBRATION AND TRAJECTORY ENHANCEMENT OF UAV AND BACKPACK LIDAR SYSTEMS FOR HIGH-RESOLUTION FOREST INVENTORY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W1-2023 (May 25, 2023): 595–602. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-595-2023.

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Abstract. Using remote sensing modalities for forest inventory has gained increasing attention in the last few decades. However, tools for deriving accurate tree-level metrics are limited. This paper investigates the feasibility of using LiDAR units onboard uncrewed aerial vehicle (UAV) and Backpack mobile mapping systems (MMS) equipped with an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) to provide high quality point clouds for accurate, high-resolution forest inventory. To improve the quality of acquired point clouds, a system-driven strategy for mounti

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Wang, Zhenyu, Lu Xiong, and Zhuoping Yu. "An Asymmetric Selective Kernel Network for Drone-Based Vehicle Detection to Build a High-Accuracy Vehicle Trajectory Dataset." Remote Sensing 17, no. 3 (2025): 407. https://doi.org/10.3390/rs17030407.

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To improve the detection accuracy of the drone-based oriented vehicle object detection network and establish high-accuracy vehicle trajectory datasets, we present a freeway on-ramp vehicle (FRVehicle) detection dataset with oriented bounding box annotations for vehicles in freeway on-ramp scenes from drone videos. Based on this dataset, we analyzed the dimension and angle distribution patterns of road vehicle object oriented bounding boxes and designed an Asymmetric Selective Kernel Network. This algorithm dynamically adjusts the receptive field of the backbone network’s feature extraction to

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Elseicy, Ahmed, Shayan Nikoohemat, Michael Peter, and Sander Oude Elberink. "Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory." Remote Sensing 10, no. 11 (2018): 1815. http://dx.doi.org/10.3390/rs10111815.

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State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner tra

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Deng, Yingjian, Li Zhang, Jie Chen, et al. "Pedestrian Trajectory Prediction Based on Motion Pattern De-Perturbation Strategy." Electronics 13, no. 6 (2024): 1135. http://dx.doi.org/10.3390/electronics13061135.

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Pedestrian trajectory prediction is extremely challenging due to the complex social attributes of pedestrians. Introducing latent vectors to model trajectory multimodality has become the latest mainstream solution idea. However, previous approaches have overlooked the effects of redundancy that arise from the introduction of latent vectors. Additionally, they often fail to consider the inherent interference of pedestrians with no trajectory history during model training. This results in the model’s inability to fully utilize the training data. Therefore, we propose a two-stage motion pattern d

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Yu, Xinwei. "Fusionformer: Exploiting the joint motion synergy with fusion network based on transformer for 3D human pose estimation." Journal of Physics: Conference Series 2786, no. 1 (2024): 012015. http://dx.doi.org/10.1088/1742-6596/2786/1/012015.

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Abstract This paper proposes a 2D-3D supervised Fusionformer method for current 3D human pose estimation. It introduces self-trajectory module and cross-trajectory module to capture the motion differences and synergy of different joints. In addition, the created Global Local Fusion Block (GLF) combines global spatio-temporal pose features and local joint trajectory features in parallel. Furthermore, to eliminate the impact of poor 2D poses on 3D projection, a pose refinement network is introduced to balance the consistency of the 3D projection. Finally, the proposed method is evaluated on two

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Manrique-Cordoba, Juliana, Miguel Ángel de la Casa-Lillo, and José María Sabater-Navarro. "N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning." Sensors 25, no. 7 (2025): 2145. https://doi.org/10.3390/s25072145.

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This paper presents an n-dimensional reduction algorithm for Learning from Demonstration (LfD) for robotic path planning, addressing the complexity of high-dimensional data. The method extends the Douglas–Peucker algorithm by incorporating velocity and orientation alongside position, enabling more precise trajectory simplification. A magnitude-based normalization process preserves proportional relationships across dimensions, and the reduced dataset is used to train Hidden Markov Models (HMMs), where continuous trajectories are discretized into identifier sequences. The algorithm is evaluated

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