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Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Existing methods for anomaly detection based on memory-augmented autoencoder (AE) have the following drawbacks: (1) Establishing a memory bank requires additional memory space. (2) The fixed number of prototypes from subjective assumptions ignores the data feature differences and diversity. To overcome these drawbacks, we introduce DLAN-AC, a Dynamic Local Aggregation Network with Adaptive Clusterer, for anomaly detection. First, The proposed DLAN can automatically learn and aggregate high-level features from the AE to obtain more representative prototypes, while freeing up extra memory space. Second, The proposed AC can adaptively cluster video data to derive initial prototypes with prior information. In addition, we also propose a dynamic redundant clustering strategy (DRCS) to enable DLAN for automatically eliminating feature clusters that do not contribute to the construction of prototypes. Extensive experiments on benchmarks demonstrate that DLAN-AC outperforms most existing methods, validating the effectiveness of our method. Our code is publicly available at https://github.com/Beyond-Zw/DLAN-AC.

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References

  1. Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 555–560 (2008)

    Article  Google Scholar 

  2. Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: NetVLAD: CNN architecture for weakly supervised place recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5297–5307 (2016)

    Google Scholar 

  3. Astrid, M., Zaheer, M.Z., Lee, J.Y., Lee, S.I.: Learning not to reconstruct anomalies. arXiv preprint arXiv:2110.09742 (2021)

  4. Astrid, M., Zaheer, M.Z., Lee, S.I.: Synthetic temporal anomaly guided end-to-end video anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 207–214 (2021)

    Google Scholar 

  5. 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. IEEE (2009)

    Google Scholar 

  6. Cai, R., Zhang, H., Liu, W., Gao, S., Hao, Z.: Appearance-motion memory consistency network for video anomaly detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 938–946 (2021)

    Google Scholar 

  7. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Comput. Surv. 41(3), 1–58 (2009)

    Article  Google Scholar 

  8. Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: CVPR 2011, pp. 3449–3456. IEEE (2011)

    Google Scholar 

  9. Dutta, J., Banerjee, B.: Online detection of abnormal events using incremental coding length. In: Proceedings of the AAAI Conference on Artificial Intelligence (2015)

    Google Scholar 

  10. Georgescu, M.I., Barbalau, A., Ionescu, R.T., Khan, F.S., Popescu, M., Shah, M.: Anomaly detection in video via self-supervised and multi-task learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12742–12752 (2021)

    Google Scholar 

  11. Gong, D., et al.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1705–1714 (2019)

    Google Scholar 

  12. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems vol. 27 (2014)

    Google Scholar 

  13. Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 733–742 (2016)

    Google Scholar 

  14. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  15. Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3304–3311. IEEE (2010)

    Google Scholar 

  16. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019)

    Google Scholar 

  17. Kim, J., Grauman, K.: Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2928. IEEE (2009)

    Google Scholar 

  18. Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)

    Article  Google Scholar 

  19. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097–1105 (2012)

    Google Scholar 

  20. Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 18–32 (2013)

    Google Scholar 

  21. Liu, W., Luo, W., Lian, D., Gao, S.: Future frame prediction for anomaly detection-a new baseline. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6536–6545 (2018)

    Google Scholar 

  22. Liu, Z., Nie, Y., Long, C., Zhang, Q., Li, G.: A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13588–13597 (2021)

    Google Scholar 

  23. Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in MATLAB. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2720–2727 (2013)

    Google Scholar 

  24. Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in MATLAB. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2720–2727 (2013)

    Google Scholar 

  25. Luo, W., Liu, W., Gao, S.: Remembering history with convolutional LSTM for anomaly detection. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 439–444. IEEE (2017)

    Google Scholar 

  26. Luo, W., Liu, W., Gao, S.: A revisit of sparse coding based anomaly detection in stacked rnn framework. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 341–349 (2017)

    Google Scholar 

  27. Lv, H., Chen, C., Cui, Z., Xu, C., Li, Y., Yang, J.: Learning normal dynamics in videos with meta prototype network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15425–15434 (2021)

    Google Scholar 

  28. Mathieu, M., Couprie, C., LeCun, Y.: Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440 (2015)

  29. Nguyen, T.N., Meunier, J.: Anomaly detection in video sequence with appearance-motion correspondence. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1273–1283 (2019)

    Google Scholar 

  30. Nie, X., Jing, W., Cui, C., Zhang, C.J., Zhu, L., Yin, Y.: Joint multi-view hashing for large-scale near-duplicate video retrieval. IEEE Trans. Knowl. Data Eng. 32(10), 1951–1965 (2019)

    Article  Google Scholar 

  31. Park, H., Noh, J., Ham, B.: Learning memory-guided normality for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14372–14381 (2020)

    Google Scholar 

  32. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  33. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-CNN: Towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)

    Google Scholar 

  34. Sabokrou, M., Fathy, M., Hoseini, M., Klette, R.: Real-time anomaly detection and localization in crowded scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 56–62 (2015)

    Google Scholar 

  35. Shao, F., Liu, J., Wu, P., Yang, Z., Wu, Z.: Exploiting foreground and background separation for prohibited item detection in overlapping x-ray images. Pattern Recogn. 122, 108261 (2022)

    Article  Google Scholar 

  36. Tang, Y., Zhao, L., Zhang, S., Gong, C., Li, G., Yang, J.: Integrating prediction and reconstruction for anomaly detection. Pattern Recogn. Lett. 129, 123–130 (2020)

    Article  Google Scholar 

  37. Tudor Ionescu, R., Smeureanu, S., Alexe, B., Popescu, M.: Unmasking the abnormal events in video. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2895–2903 (2017)

    Google Scholar 

  38. Van Der Maaten, L.: Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15(1), 3221–3245 (2014)

    MathSciNet  MATH  Google Scholar 

  39. Wang, X., et al.: Robust unsupervised video anomaly detection by multipath frame prediction. IEEE Trans. Neural Netw. Learn. Syst. 33 (2021)

    Google Scholar 

  40. Wang, Z., Zou, Y., Zhang, Z.: Cluster attention contrast for video anomaly detection. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2463–2471 (2020)

    Google Scholar 

  41. Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp. 3–19 (2018)

    Google Scholar 

  42. Wu, P., Liu, J.: Learning causal temporal relation and feature discrimination for anomaly detection. IEEE Trans. Image Process. 30, 3513–3527 (2021). https://doi.org/10.1109/TIP.2021.3062192

    Article  Google Scholar 

  43. Wu, P., Liu, J., Shen, F.: A deep one-class neural network for anomalous event detection in complex scenes. IEEE Trans. Neural Netw. Learn. Syst. 31(7), 2609–2622 (2019)

    Google Scholar 

  44. Wu, P., et al.: Not only look, but also listen: learning multimodal violence detection under weak supervision. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12375, pp. 322–339. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58577-8_20

    Chapter  Google Scholar 

  45. Xia, C., Qi, F., Shi, G.: Bottom-up visual saliency estimation with deep autoencoder-based sparse reconstruction. IEEE Trans. Neural Netw. Learn. Syst. 27(6), 1227–1240 (2016)

    Article  MathSciNet  Google Scholar 

  46. Xu, D., Ricci, E., Yan, Y., Song, J., Sebe, N.: Learning deep representations of appearance and motion for anomalous event detection. arXiv preprint arXiv:1510.01553 (2015)

  47. Yang, Z., Liu, J., Wu, P.: Bidirectional retrospective generation adversarial network for anomaly detection in videos. IEEE Access 9, 107842–107857 (2021)

    Article  Google Scholar 

  48. Ye, M., Peng, X., Gan, W., Wu, W., Qiao, Y.: AnoPCN: video anomaly detection via deep predictive coding network. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1805–1813 (2019)

    Google Scholar 

  49. Zhai, S., Cheng, Y., Lu, W., Zhang, Z.: Deep structured energy based models for anomaly detection. In: International Conference on Machine Learning, pp. 1100–1109. PMLR (2016)

    Google Scholar 

  50. Zhao, B., Fei-Fei, L., Xing, E.P.: Online detection of unusual events in videos via dynamic sparse coding. In: CVPR 2011, pp. 3313–3320. IEEE (2011)

    Google Scholar 

  51. Zhao, Y., Deng, B., Shen, C., Liu, Y., Lu, H., Hua, X.S.: Spatio-temporal autoencoder for video anomaly detection. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 1933–1941 (2017)

    Google Scholar 

  52. Zhong, Y., Arandjelović, R., Zisserman, A.: GhostVLAD for set-based face recognition. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11362, pp. 35–50. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20890-5_3

    Chapter  Google Scholar 

  53. Zhou, J.T., Du, J., Zhu, H., Peng, X., Liu, Y., Goh, R.S.M.: Anomalynet: an anomaly detection network for video surveillance. IEEE Trans. Inf. Forensics Secur. 14(10), 2537–2550 (2019)

    Article  Google Scholar 

  54. Zong, B., et al.: Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)

    Google Scholar 

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Acknowledgments

This work was supported in part by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China under Grant 2018AAA0101302.

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Correspondence to Peng Wu or Jing Liu .

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Yang, Z., Wu, P., Liu, J., Liu, X. (2022). Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13664. Springer, Cham. https://doi.org/10.1007/978-3-031-19772-7_24

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  • DOI: https://doi.org/10.1007/978-3-031-19772-7_24

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