Relevant bibliographies by topics / Trajectory anomaly detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Trajectory anomaly detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Trajectory anomaly detection"

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Praczyk, Tomasz. "Ship trajectory anomaly detection." Intelligent Data Analysis 23, no. 5 (2019): 1021–40. http://dx.doi.org/10.3233/ida-184366.

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Zhang, Haiyan, Yonglong Luo, Qingying Yu, Liping Sun, Xuejing Li, and Zhenqiang Sun. "A Framework of Abnormal Behavior Detection and Classification Based on Big Trajectory Data for Mobile Networks." Security and Communication Networks 2020 (December 22, 2020): 1–15. http://dx.doi.org/10.1155/2020/8858444.

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Big trajectory data feature analysis for mobile networks is a popular big data analysis task. Due to the large coverage and complexity of the mobile networks, it is difficult to define and detect anomalies in urban motion behavior. Some existing methods are not suitable for the detection of abnormal urban vehicle trajectories because they use the limited single detection techniques, such as determining the common patterns. In this study, we propose a framework for urban trajectory modeling and anomaly detection. Our framework takes into account the fact that anomalous behavior manifests the ov
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Shuaau, Muhammed, and Ka Fei Thang. "Autonomous Anomaly Detection System for Crime Monitoring." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3410–18. http://dx.doi.org/10.1166/jctn.2019.8301.

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Autonomous anomaly detection has attracted significant amount of attention in the past decade due to increased security concerns all around the world. The volume of data reported by surveillance cameras has outrun human capacity and there exists a greater need for anomaly detection systems for crime monitoring. This project proposes a solution to this problem in a reception area context by using trajectory analysis. Trajectory extraction is proposed by using Gaussian Mixture Models and Kalman Filter for data association. Then trajectory analysis is performed on extracted trajectories to detect
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Ando, Shin, Theerasak Thanomphongphan, Yoichi Seki, and Einoshin Suzuki. "Ensemble anomaly detection from multi-resolution trajectory features." Data Mining and Knowledge Discovery 29, no. 1 (2013): 39–83. http://dx.doi.org/10.1007/s10618-013-0334-x.

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Shuaau, Muhammed, Ka Fei Thang, and Nai Shyan Lai. "Autonomous Abnormal Behaviour Detection Using Trajectory Analysis." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 2403. http://dx.doi.org/10.11591/ijece.v9i4.pp2403-2415.

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<span lang="EN-GB">Abnormal behaviour detection has attracted signification amount of attention in the past decade due to increased security concerns around the world. The amount of data from surveillance cameras have exceeded human capacity and there is a greater need for anomaly detection systems for crime monitoring. This paper proposes a solution to this problem in a reception area context by using trajectory extraction through Gaussian Mixture Models and Kalman Filter for data association. Here, trajectory analysis was performed on extracted trajectories to detect four different ano
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Deng, Ketao. "Anomaly Detection of Highway Vehicle Trajectory under the Internet of Things Converged with 5G Technology." Complexity 2021 (April 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/9961428.

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The gradual increase in the density of highway vehicles and traffic flow makes the abnormal driving state of vehicles an indispensable tool for assisting traffic dispatch. Intelligent transportation systems can detect and track vehicles in real time, acquire characteristics such as vehicle traffic, vehicle speed, vehicle flow density, and vehicle trajectory, and further perform advanced tasks such as vehicle trajectory. The detection of abnormal vehicle trajectory is an important content of vehicle trajectory understanding. And the development of the Internet of Things (IoT) and 5G technology
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Chebiyyam, Manaswi, Rohit Desam Reddy, Debi Prosad Dogra, Harish Bhaskar, and Lyudmila Mihaylova. "Motion anomaly detection and trajectory analysis in visual surveillance." Multimedia Tools and Applications 77, no. 13 (2017): 16223–48. http://dx.doi.org/10.1007/s11042-017-5196-6.

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Lei, Po-Ruey. "A framework for anomaly detection in maritime trajectory behavior." Knowledge and Information Systems 47, no. 1 (2015): 189–214. http://dx.doi.org/10.1007/s10115-015-0845-4.

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Peltola, Pekka, Jialin Xiao, Terry Moore, Antonio Jiménez, and Fernando Seco. "GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation." Sensors 18, no. 9 (2018): 3165. http://dx.doi.org/10.3390/s18093165.

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The urban setting is a challenging environment for GNSS receivers. Multipath and other anomalies typically increase the positioning error of the receiver. Moreover, the error estimate of the position is often unreliable. In this study, we detect GNSS trajectory anomalies by using similarity comparison methods between a pedestrian dead reckoning trajectory, recorded using a foot-mounted inertial measurement unit, and the corresponding GNSS trajectory. During a normal walk, the foot-mounted inertial dead reckoning setup is trustworthy up to a few tens of meters. Thus, the differing GNSS trajecto
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Guo, Yuejun, and Anton Bardera. "SHNN-CAD+: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection." Sensors 19, no. 1 (2018): 84. http://dx.doi.org/10.3390/s19010084.

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To perform anomaly detection for trajectory data, we study the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) approach, and propose an enhanced version called SHNN-CAD +. SHNN-CAD was introduced based on the theory of conformal prediction dealing with the problem of online detection. Unlike most related approaches requiring several not intuitive parameters, SHNN-CAD has the advantage of being parameter-light which enables the easy reproduction of experiments. We propose to adaptively determine the anomaly threshold during the online detection procedure instead of p
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Dissertations / Theses on the topic "Trajectory anomaly detection"

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Laxhammar, Rikard. "Anomaly detection in trajectory data for surveillance applications." Licentiate thesis, Örebro universitet, Akademin för naturvetenskap och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-17235.

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Abnormal behaviour may indicate important objects and events in a wide variety of domains. One such domain is intelligence and surveillance, where there is a clear trend towards more and more advanced sensor systems producing huge amounts of trajectory data from moving objects, such as people, vehicles, vessels and aircraft. In the maritime domain, for example, abnormal vessel behaviour, such as unexpected stops, deviations from standard routes, speeding, traffic direction violations etc., may indicate threats and dangers related to smuggling, sea drunkenness, collisions, grounding, hijacking,
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Laxhammar, Rikard. "Conformal anomaly detection : Detecting abnormal trajectories in surveillance applications." Doctoral thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8762.

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Human operators of modern surveillance systems are confronted with an increasing amount of trajectory data from moving objects, such as people, vehicles, vessels, and aircraft. A large majority of these trajectories reflect routine traffic and are uninteresting. Nevertheless, some objects are engaged in dangerous, illegal or otherwise interesting activities, which may manifest themselves as unusual and abnormal trajectories. These anomalous trajectories can be difficult to detect by human operators due to cognitive limitations. In this thesis, we study algorithms for the automated detection of
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Koňárek, Petr. "Dolování neobvyklého chování v datech trajektorií." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363796.

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The goal of this work is to provide an overview of approaches for mining anomalous behavior in trajectory data. Next part is proposes a mining task for outliner detection in trajectories and selects appropriate methods for this task. Selected methods are implemented as application for outliner trajectories detection.
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Bartl, Vojtěch. "Mapování pohybu osob stacionární kamerou." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-235002.

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The aim of this diploma thesis is to obtain information on the motion of people in a scene from the record of the stationary camera. The procedure to detect exceptional events in the scene was designed. Exceptional events can be fast-moving persons, or persons moving in di erent places than everyone else in the scene. To trace the motion of persons, two algorithms were applied and tested - Optical flow and CAMSHIFT. The analysis of the resulting motions is performed by monitoring the progress of motion, and its comparison with the other motions in the scene. The analysis result is represented
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Chen, Chao. "Understanding social and community dynamics from taxi GPS data." Phd thesis, Institut National des Télécommunications, 2014. http://tel.archives-ouvertes.fr/tel-01048662.

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Taxis equipped with GPS sensors are an important sensory device for examining people's movements and activities. They are not constrained to a pre-defined schedule/route. Big taxi GPS data recording the spatio-temporal traces left by taxis provides rich and detailed glimpse into the motivations, behaviours, and resulting dynamics of a city's mobile population through the road network. In this dissertation, we aim to uncover the "hidden facets" regarding social and community dynamics encoded in the taxi GPS data to better understand how urban population behaves and the resulting dynamics in the
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Pešek, Martin. "Získávání znalostí z časoprostorových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237048.

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This thesis deals with knowledge discovery in spatio-temporal data, which is currently a rapidly evolving area of research in information technology. First, it describes the general principles of knowledge discovery, then, after a brief introduction to mining in the temporal and spatial data, it focuses on the overview and description of existing methods for mining in spatio-temporal data. It focuses, in particular, on moving objects data in the form of trajectories with an emphasis on the methods for trajectory outlier detection. The next part of the thesis deals with the process of implement
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Chen, Pei-Chen, and 陳佩志. "Maritime Vessels Trajectory Pattern Analysis and Anomaly Detection." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/pe7497.

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碩士<br>國立臺灣大學<br>地理環境資源學研究所<br>106<br>Currently, the Coast Guard Administration of the Executive Yuan carries out maritime surveillance tasks by analyzing and judging the abnormality of the ship&apos;&apos;s movement behavior through the experience of the monitoring personnel. In the face of the continuous generation and increase of many ship movement trajectory data, the task of monitoring personnel performing marine traffic monitoring is even more onerous. On the other hand, for relatively few abnormal movements, the more difficult it is to detect and judge through human resources. However, w
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彭靖鑑. "A Trajectory Model Based Anomaly Detection for Moving Objects." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/90751858966985690155.

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碩士<br>國立交通大學<br>資訊學院資訊學程<br>100<br>Global Positioning System(GPS）almost becomes a powerful tool in our daily life. We can see the application of GPS in all the conveyance. The GPS does not only provide for the positioning and navigation function, but also predict the anomaly trajectory by its trajectory data which the GPS provide.In this paper, we implemented a trajectory model based on anomaly detection by moving objects. After this model built, we could input the real-time trajectory data to the trajectory pattern tree model to see if it shows an anomaly trajectory.In the end of this paper,
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Fernandes, Letícia Maria Sousa. "Learning Human Behaviour Patterns by Trajectory and Activity Recognition." Master's thesis, 2019. http://hdl.handle.net/10362/87075.

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The world’s population is ageing, increasing the awareness of neurological and behavioural impairments that may arise from the human ageing. These impairments can be manifested by cognitive conditions or mobility reduction. These conditions are difficult to be detected on time, relying only on the periodic medical appointments. Therefore, there is a lack of routine screening which demands the development of solutions to better assist and monitor human behaviour. The available technologies to monitor human behaviour are limited to indoors and require the installation of sensors around the
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Book chapters on the topic "Trajectory anomaly detection"

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Wu, Ruizhi, Guangchun Luo, Qing Cai, and Chunyu Wang. "Anomaly Detection via Trajectory Representation." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1328-8_7.

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Li, Chengcheng, Qing Xu, Cheng Peng, and Yuejun Guo. "Anomaly Detection Based on the Global-Local Anomaly Score for Trajectory Data." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36802-9_30.

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Hu, Xiaoyuan, Qing Xu, and Yuejun Guo. "Trajectory Anomaly Detection Based on the Mean Distance Deviation." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63820-7_16.

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Cai, Wen-Yu, Zi-Qiang Liu, and Mei-Yan Zhang. "Trajectory Clustering Based Oceanic Anomaly Detection Using Argo Profile Floats." In Communications and Networking. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41114-5_37.

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Mondal, Suman, Arindam Roy, and Sukumar Mandal. "A Supervised Trajectory Anomaly Detection Using Velocity and Path Deviation." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7834-2_72.

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Huang, Zejun, Jian Wan, Jie Huang, Gangyong Jia, and Wei Zhang. "A Collaborative Anomaly Detection Approach of Marine Vessel Trajectory (Short Paper)." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30146-0_19.

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Cheng, Bin, Shiyou Qian, Jian Cao, et al. "STL: Online Detection of Taxi Trajectory Anomaly Based on Spatial-Temporal Laws." In Database Systems for Advanced Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18579-4_45.

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Kałużny, Piotr. "Behavioural Profiling Authentication Based on Trajectory Based Anomaly Detection Model of User’s Mobility." In Business Information Systems Workshops. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69023-0_21.

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Li, Li, and Christopher Leckie. "Trajectory Pattern Identification and Anomaly Detection of Pedestrian Flows Based on Visual Clustering." In Intelligent Information Processing VIII. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48390-0_13.

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Conference papers on the topic "Trajectory anomaly detection"

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Ogrenci, Arif Selcuk. "Anomaly detection in walking trajectory." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404797.

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Zhang, Zhengchao, Meng Li, Fang He, and Yinhai Wang. "Clustering Approach for Trajectory Anomaly Detection." In 20th COTA International Conference of Transportation Professionals. American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784483053.010.

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Faderl, Norbert. "3D trajectory reconstruction of fast moving objects under harsh conditions using flash radiography imaging." In Anomaly Detection and Imaging with X-Rays (ADIX) V, edited by Amit Ashok, Michael E. Gehm, and Joel A. Greenberg. SPIE, 2020. http://dx.doi.org/10.1117/12.2558232.

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Maiorano, Francesco, and Alfredo Petrosino. "Granular trajectory based anomaly detection for surveillance." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7899940.

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Anneken, Mathias, Anne-Laure Jousselme, Sebastian Robert, and Jurgen Beyerer. "Synthetic Trajectory Extraction for Maritime Anomaly Detection." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00204.

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Fan, Xinnan, Bingbin Zheng, Min Li, Weilong Li, Ji Zhang, and Zhuo Zhang. "Characterization for complex trajectory and anomaly detection." In 2014 International Conference on Information Science, Electronics and Electrical Engineering (ISEEE). IEEE, 2014. http://dx.doi.org/10.1109/infoseee.2014.6947761.

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Biswas, Sovan, and R. Venkatesh Babu. "Short Local Trajectory based moving anomaly detection." In the 2014 Indian Conference. ACM Press, 2014. http://dx.doi.org/10.1145/2683483.2683556.

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Datlica, Mustafa Tolga, and Engin Demir. "Anomaly Detection in Location and Trajectory Datasets." In 2021 29th Signal Processing and Communications Applications Conference (SIU). IEEE, 2021. http://dx.doi.org/10.1109/siu53274.2021.9477872.

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Murray, Brian, and Lokukaluge P. Perera. "Unsupervised Trajectory Anomaly Detection for Situation Awareness in Maritime Navigation." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18281.

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Abstract Situation awareness is essential in conducting effective collision avoidance in potential ship encounter situations. It has been shown that data driven trajectory prediction techniques, utilizing historical AIS data, have the potential to aid in providing such awareness. However, such data driven techniques will not perform well for unusual ship behavior, i.e. anomalous trajectories. Additionally, such anomalies in the dataset can corrupt the predictions. In this study, an unsupervised approach to anomaly detection is presented to aid such trajectory predictions. Gaussian Mixture Mode
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Dias, Madson L. D., Cesar Lincoln C. Mattos, Ticiana L. C. da Silva, Jose Antonio F. de Macedo, and Wellington C. P. Silva. "Anomaly Detection in Trajectory Data with Normalizing Flows." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206939.

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