Abstract
We propose a novel unsupervised approach based on a two-stage object-centric adversarial framework that only needs object regions for detecting frame-level local anomalies in videos. The first stage consists in learning the correspondence between the current appearance and past gradient images of objects in scenes deemed normal, allowing us to either generate the past gradient from current appearance or the reverse. The second stage extracts the partial reconstruction errors between real and generated images (appearance and past gradient) with normal object behaviour, and trains a discriminator in an adversarial fashion. In inference mode, we employ the trained image generators with the adversarially learned binary classifier for outputting region-level anomaly detection scores. We tested our method on four public benchmarks, UMN, UCSD, Avenue and ShanghaiTech and our proposed object-centric adversarial approach yields competitive or even superior results compared to state-of-the-art methods.
Supported by grants from IVADO and NSERC funding programs.
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References
Del Giorno, A., Bagnell, J.A., Hebert, M.: A discriminative framework for anomaly detection in large videos. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 334–349. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_21
Feng, J.: A implementation of Object-Centric VAD using Tensorflow (2019). https://github.com/fjchange/object_centric_VAD
Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: CVPR. IEEE (2016)
Ionescu, R.T., Khan, F.S., Georgescu, M.I., Shao, L.: Object-centric auto-encoders and dummy anomalies for abnormal event detection in video. In: CVPR. IEEE (2019)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Pix2Pix. In: CVPR. IEEE (2017)
Kim, T., Cha, M., Kim, H., Lee, J.K., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks. In: ICML. JMLR.org, March 2017
Li, C., Han, Z., Ye, Q., Jiao, J.: Visual abnormal behavior detection based on trajectory sparse reconstruction analysis. Neurocomputing 119, 94–100 (2013)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: ICCV. IEEE (2017)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, W., Luo, W., Lian, D., Gao, S.: Future frame prediction for anomaly detection - a new baseline. In: CVPR. IEEE (2018)
Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 FPS in MATLAB. In: ICCV. IEEE (2013)
Luo, W., Liu, W., Gao, S.: A revisit of sparse coding based anomaly detection in stacked RNN framework. In: ICCV. IEEE (2017)
Luo, W., Liu, W., Gao, S.: Remembering history with convolutional LSTM for anomaly detection. In: ICME. IEEE (2017)
Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: CVPR. IEEE (2010)
Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: CVPR Workshops. IEEE (2009)
Mousavi, H., Mohammadi, S., Perina, A., Chellali, R., Murino, V.: Analyzing tracklets for the detection of abnormal crowd behavior. In: WACV. IEEE (2015)
Nguyen, T.N., Meunier, J.: Anomaly detection in video sequence with appearance-motion correspondence. In: ICCV. IEEE (2019)
Ribeiro, M., Lazzaretti, A.E., Lopes, H.S.: A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recogn. Lett. 105, 13–22 (2018)
Roy, P., Bilodeau, G.A.: Adversarially learned abnormal trajectory classifier. In: CRV. IEEE (2019)
Roy, P.R., Bilodeau, G.-A.: Road user abnormal trajectory detection using a deep autoencoder. In: Bebis, G., et al. (eds.) ISVC 2018. LNCS, vol. 11241, pp. 748–757. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03801-4_65
Sabokrou, M., Fayyaz, M., Fathy, M., Klette, R.: Deep-cascade: cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes. IEEE Trans. Image Process. 26, 1992–2004 (2017)
Sabokrou, M., et al.: AVID: adversarial visual irregularity detection. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11366, pp. 488–505. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20876-9_31
Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13, 1443–1471 (2001)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. CoRR (2016)
Wang, Z., Hou, C., Li, B., Chen, T., Yao, L., Song, M.: Global abnormal event detection in video via motion information entropy. In: AT-RASC (2018)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)
Zhao, Y., Deng, B., Shen, C., Liu, Y., Lu, H., Hua, X.S.: Spatio-temporal AutoEncoder for video anomaly detection. In: ACM on Multimedia Conference. ACM Press (2017)
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. CoRR (2019)
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Roy, P.R., Bilodeau, GA., Seoud, L. (2021). Local Anomaly Detection in Videos Using Object-Centric Adversarial Learning. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_16
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