Abstract
In this paper, we focus on the development of a method that detects abnormal trajectories of road users at traffic intersections. The main difficulty with this is the fact that there are very few abnormal data and the normal ones are insufficient for the training of any kinds of machine learning model. To tackle these problems, we proposed the solution of using a deep autoencoder network trained solely through augmented data considered as normal. By generating artificial abnormal trajectories, our method is tested on four different outdoor urban users scenes and performs better compared to some classical outlier detection methods.
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References
Benezeth, Y., Jodoin, P.M., Saligrama, V., Rosenberger, C.: Abnormal events detection based on spatio-temporal co-occurences. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2458–2465, June 2009. https://doi.org/10.1109/CVPR.2009.5206686
Cui, X., Liu, Q., Gao, M., Metaxas, D.N.: Abnormal detection using interaction energy potentials. In: CVPR 2011, pp. 3161–3167 (2011). https://doi.org/10.1109/CVPR.2011.5995558
Jodoin, J.P., Bilodeau, G.A., Saunier, N.: Urban tracker: multiple object tracking in urban mixed traffic. In: IEEE Winter Conference on Applications of Computer Vision, pp. 885–892, March 2014. https://doi.org/10.1109/WACV.2014.6836010
Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422, December 2008. https://doi.org/10.1109/ICDM.2008.17
Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in matlab. In: 2013 IEEE International Conference on Computer Vision, pp. 2720–2727, December 2013. https://doi.org/10.1109/ICCV.2013.338
Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1975–1981, June 2010. https://doi.org/10.1109/CVPR.2010.5539872
Mousavi, H., Mohammadi, S., Perina, A., Chellali, R., Murino, V.: Analyzing tracklets for the detection of abnormal crowd behavior. In: 2015 IEEE Winter Conference on Applications of Computer Vision, pp. 148–155, January 2015. https://doi.org/10.1109/WACV.2015.27
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Signal Process. 99, 215–249 (2014). https://doi.org/10.1016/j.sigpro.2013.12.026. http://www.sciencedirect.com/science/article/pii/S016516841300515X
Ribeiro, M., Lazzaretti, A.E., Lopes, H.S.: A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recognit. Lett. 105, 13–22 (2018). https://doi.org/10.1016/j.patrec.2017.07.016. http://www.sciencedirect.com/science/article/pii/S0167865517302489. Machine Learning and Applications in Artificial Intelligence
Schölkopf, B., Platt, J.C., Shawe-Taylor, J.C., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001). https://doi.org/10.1162/089976601750264965
Vignesh, K., Yadav, G., Sethi, A.: Abnormal event detection on BMTT-PETS 2017 surveillance challenge. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2161–2168, July 2017. https://doi.org/10.1109/CVPRW.2017.268
Wang, T., Qiao, M., Zhu, A., Niu, Y., Li, C., Snoussi, H.: Abnormal event detection via covariance matrix for optical flow based feature. Multimed. Tools Appl. 77, 17375–17395 (2017). https://doi.org/10.1007/s11042-017-5309-2
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This research was supported by a grant from IVADO funding program for fundamental research projects.
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Roy, P.R., Bilodeau, GA. (2018). Road User Abnormal Trajectory Detection Using a Deep Autoencoder. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_65
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DOI: https://doi.org/10.1007/978-3-030-03801-4_65
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