Abstract
This paper presents an approach for motion-based anomaly detection, where a prototype pattern is detected and elastically registered against a test sample to detect anomalies in the test sample. The prototype model is learned from multiple sequences to define accepted variations. “Supertrajectories” based on hierarchical clustering of dense point trajectories serve as an efficient and robust representation of motion patterns. An efficient hashing approach provides transformation hypotheses that are refined by a spatiotemporal elastic registration. We propose a new method for elastic registration of 3D+time trajectory patterns that induces spatial elasticity from trajectory affinities. The method is evaluated on a new motion anomaly dataset of juggling patterns and performs well in detecting subtle anomalies. Moreover, we demonstrate the applicability to biological motion patterns.
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Unusual crowd activity dataset made available by the University of Minnesota at: http://mha.cs.umn.edu/Movies/Crowd-Activity-All.avi.
We thank the authors for providing essential code pieces to reimplement their method.
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Acknowledgments
We thank J. Koch, A. Krämer, T. Paxian and D. Mai who contributed their juggling expertise and agreed to perform diverse juggling patterns in front of our Kinect camera. This study was supported by the Excellence Initiative of the German Federal and State Governments (BIOSS Centre for Biological Signalling Studies EXC 294 to R.B., T.B. and O.R.). N.S. and J.H. have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 647885).
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Communicated by Xianghua Xie, Mark Jones, Gary Tam.
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Appendix: Juggling Patterns Dataset Overview
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Bensch, R., Scherf, N., Huisken, J. et al. Spatiotemporal Deformable Prototypes for Motion Anomaly Detection. Int J Comput Vis 122, 502–523 (2017). https://doi.org/10.1007/s11263-016-0934-1
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DOI: https://doi.org/10.1007/s11263-016-0934-1