Abstract
Detecting anomalies from trajectory data is an important task in video surveillance. However, it is difficult to give a precise definition of this term since trajectory data obtained from different camera views may vary in shape, direction, and spatial distribution. In this paper, we propose trajectory distance metrics based on a recurrent neural network to measure similarities and detect anomalies from trajectory data. First, we use an autoencoder to capture the dynamic features of a trajectory. The distance between two trajectories is defined by the reconstruction errors based on the learned models. We then detect anomalies based on the nearest neighbors using the proposed metric. As such, we can deal with various kinds of anomalies in different scenes and detect anomalous trajectories in either a supervised or unsupervised manner. Experiments show that the proposed algorithm performs favorably against the state-of-the-art anomaly detections on the benchmark datasets.
This work is supported in part by NSFC (No. 61672089, 61273274, and 61572064), National Key Technology R&D Program of China 2012BAH01F03, the Fundamental Research Funds for the Central Universities 2017YJS043, the NSF CAREER Grant (No. 1149783), and gifts from Adobe and Nvidia. Cong Ma and Shaoyue Song are supported by a scholarship from China Scholarship Council.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alt, H., Godau, M.: Computing the fréchet distance between two polygonal curves. Int. J. Comput. Geom. Appl. 5, 75–91 (1995)
Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Workshop on Knowledge Discovery in Databases (1994)
Besse, P., Guillouet, B., Loubes, J.M., François, R.: Review and perspective for distance based trajectory clustering. arXiv preprint arXiv:1508.04904 (2015)
Chen, L., Ng, R.: On the marriage of LP-norms and edit distance. In: International Conference on Very Large Data Bases (2004)
Chen, L., Ozsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: ACM SIGMOD International Conference on Management of Data (2005)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Chong, Y.S., Tay, Y.H.: Abnormal event detection in videos using spatiotemporal autoencoder. arXiv preprint arXiv:1701.01546 (2017)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Ergezer, H., Leblebicioğlu, K.: Anomaly detection and activity perception using covariance descriptor for trajectories. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 728–742. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_51
Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)
Hausdorff, F.: Grundz uge der mengenlehre (1914)
Hu, W., Li, X., Tian, G., Maybank, S., Zhang, Z.: An incremental DPMM-based method for trajectory clustering, modeling, and retrieval. IEEE Trans. Pattern Mach. Intell. 35(5), 1051–1065 (2013)
Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., Maybank, S.: A system for learning statistical motion patterns. IEEE Trans. Pattern Mach. Intell. 28(9), 1450–1464 (2006)
Jiang, F., Wu, Y., Katsaggelos, A.K.: A dynamic hierarchical clustering method for trajectory-based unusual video event detection. IEEE Trans. Image Process. 18(4), 907–913 (2009)
Kumar, D., Bezdek, J.C., Rajasegarar, S., Leckie, C., Palaniswami, M.: A visual-numeric approach to clustering and anomaly detection for trajectory data. Vis. Comput. 33(3), 265–281 (2017)
Laxhammar, R., Falkman, G.: Online learning and sequential anomaly detection in trajectories. IEEE Trans. Pattern Mach. Intell. 36(6), 1158–1173 (2014)
Li, C., Han, Z., Ye, Q., Jiao, J.: Visual abnormal behavior detection based on trajectory sparse reconstruction analysis. Neurocomputing 119, 94–100 (2013)
Lin, W., et al.: A tube-and-droplet-based approach for representing and analyzing motion trajectories. IEEE Trans. Pattern Mach. Intell. 39(8), 1489–1503 (2017)
Morris, B.T., Trivedi, M.M.: Trajectory learning for activity understanding: Unsupervised, multilevel, and long-term adaptive approach. IEEE Trans. Pattern Mach. Intell. 33(11), 2287–2301 (2011)
Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-based anomalous event detection. IEEE Trans. Circ. Syst. Video Technol. 18(11), 1544–1554 (2008)
Porikli, F.: Trajectory distance metric using hidden Markov model based representation. In: ECCV 2004, PETS Workshop (2004)
Sillito, R.R., Fisher, R.B.: Semi-supervised learning for anomalous trajectory detection. In: British Machine Vision Conference (2008)
Srivastava, N., Mansimov, E., Salakhudinov, R.: Unsupervised learning of video representations using LSTMs. In: International Conference on Machine Learning (2015)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Neural Information Processing Systems (2014)
Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: IEEE International Conference on Data Engineering (2002)
Wen, L., et al.: UA-DETRAC: a new benchmark and protocol for multi-object detection and tracking. arXiv preprint arXiv:1511.04136 (2015)
Yang, H., Wang, B., Lin, S., Wipf, D., Guo, M., Guo, B.: Unsupervised extraction of video highlights via robust recurrent auto-encoders. In: IEEE International Conference on Computer Vision (2015)
Yankov, D., Keogh, E., Rebbapragada, U.: Disk aware discord discovery: finding unusual time series in terabyte sized datasets. In: IEEE International Conference on Data Mining, pp. 381–390 (2007)
Yao, D., Zhang, C., Zhu, Z., Huang, J., Bi, J.: Trajectory clustering via deep representation learning. In: International Joint Conference on Neural Networks (2017)
Zhang, Z., Huang, K., Tan, T.: Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In: International Conference on Pattern Recognition (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ma, C., Miao, Z., Li, M., Song, S., Yang, MH. (2019). Detecting Anomalous Trajectories via Recurrent Neural Networks. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-20870-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20869-1
Online ISBN: 978-3-030-20870-7
eBook Packages: Computer ScienceComputer Science (R0)