Abnormal work cycle detection based on dissimilarity measurement of trajectories
Authors: 
Xingzhe Xie, Dimitri Van Cauwelaert, Maarten Slembrouck, Karel Bauters, Johannes Cottyn, Dirk Van Haerenborgh, Hamid Aghajan, Peter Veelaert, Wilfried PhilipsAuthors Info & Claims
Pages 68 - 73
Abstract
This paper proposes a method for detecting the abnormalities of the executed work cycles for the factory workers using their tracks obtained in a multi-camera network. The method allows analyzing both spatial and temporal dissimilarity between the pairwise tracks. The main novelty of the methods is calculating spatial dissimilarity between pair-wise tracks by aligning them using Dynamic Time Warping (DTW) based on coordinate distance, and specially the velocity and dwell time dissimilarity using a different track alignment based on velocity difference. These dissimilarity measurements are used to cluster the executed work cycles and detect abnormalities. The experimental results show that our algorithm outperforms other methods on clustering the tracks because of the use of temporal dissimilarity.
References
[1]
Yue Zhou, Shuicheng Yan, and Thomas S. Huang. Detecting anomaly in videos from trajectory similarity analysis. In Multimedia and Expo, 2007 IEEE International Conference on, pages 1087--1090. IEEE, 2007.
[2]
Xiaogang Wang. Intelligent multi-camera video surveillance: A review. Pattern recognition letters, 34(1):3--19, 2013.
[3]
Sébastien Gebus and Kauko Leiviskä. Knowledge acquisition for decision support systems on an electronic assembly line. Expert Systems with Applications, 36(1):93--101, 2009.
[4]
Maarten Slembrouck, Dimitri Van Cauwelaert, David Van Hamme, Dirk Van Haerenborgh, Peter Van Hese, Peter Veelaert, and Wilfried Philips. Self-learning voxel-based multi-camera occlusion maps for 3d reconstruction. In 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP-2014). SCITEPRESS, 2014.
[5]
Brendan Tran Morris and Mohan M. Trivedi. A survey of vision-based trajectory learning and analysis for surveillance. Circuits and Systems for Video Technology, IEEE Transactions on, 18(8):1114--1127, 2008.
[6]
Zhouyu Fu, Weiming Hu, and Tieniu Tan. Similarity based vehicle trajectory clustering and anomaly detection. In Image Processing, 2005. ICIP 2005. IEEE International Conference on, volume 2, pages II--602. IEEE, 2005.
[7]
Claudio Piciarelli and Gian Luca Foresti. On-line trajectory clustering for anomalous events detection. Pattern Recognition Letters, 27(15):1835--1842, 2006.
[8]
Xingzhe Xie, Jonas De Vylder, Dimitri Van Cauwelaert, Peter Veelaert, Wilfried Philips, and Hamid Aghajan. Average track estimation of moving objects using ransac and dtw. In Proceedings of the 8th ACM/IEEE International Conference on Distributed Smart Cameras, pages 215--220. ACM/IEEE, 2014.
[9]
Michail Vlachos, George Kollios, and Dimitrios Gunopulos. Discovering similar multidimensional trajectories. In Data Engineering, 2002. Proceedings. 18th International Conference on, pages 673--684. IEEE, 2002.
[10]
Faisal I. Bashir, Ashfaq A. Khokhar, and Dan Schonfeld. Real-time motion trajectory-based indexing and retrieval of video sequences. Multimedia, IEEE Transactions on, 9(1):58--65, 2007.
[11]
Stefan Atev, Osama Masoud, and Nikolaos Papanikolopoulos. Learning traffic patterns at intersections by spectral clustering of motion trajectories. In Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on, pages 4851--4856. IEEE, 2006.
[12]
Zhang Zhang, Kaiqi Huang, and Tieniu Tan. Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In Pattern Recognition, 2006. ICPR 2006. 18th International Conference on, volume 3, pages 1135--1138. IEEE, 2006.
[13]
Brendan Morris and Mohan Trivedi. Learning trajectory patterns by clustering: Experimental studies and comparative evaluation. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 312--319. IEEE, 2009.
[14]
Donald J. Berndt and James Clifford. Using dynamic time warping to find patterns in time series. In KDD workshop, volume 10, pages 359--370. Seattle, WA, 1994.
Index Terms
- Abnormal work cycle detection based on dissimilarity measurement of trajectories
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- Escuela Técnica superier de Ingeniería Informática, Universidad de Seville, Spain: Escuela Técnica superier de Ingeniería Informática, Universidad de Seville, Spain
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Published: 08 September 2015
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- Research-article
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- Chinese Scholarship Council
- Ghent University
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ICDSC '15
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- Escuela Técnica superier de Ingeniería Informática, Universidad de Seville, Spain
ICDSC '15: International Conference on distributed Smart Cameras
September 8 - 11, 2015
Seville, Spain
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ICDSC '15 Paper Acceptance Rate 43 of 48 submissions, 90%;
Overall Acceptance Rate 92 of 117 submissions, 79%
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