Abstract
In view of current algorithms for the crowd abnormal behavior detection are not suitable for different scenarios, a hierarchical detection algorithm is proposed. First, the target area is extracted using the Gaussian mixture model. According to the pixel ratio, decide which type the group belongs to: individualism behaviors, social interaction behaviors or leadership-led behaviors. Then divide the video according to the category of the crowd, calculate HOG-LBP for the crowd with individualism to judge the abnormal appearance. For other categories, calculate the trajectory and entropy in the divided image to obtain the speed, deviation from the trajectory and variance of the trajectory. Then compare with the corresponding thresholds to determine whether an abnormality occurs. The value of the entropy and its first-order function are used to judge the abnormal extent. When the entropy does not exceed 3/2 of the threshold, the optical flow is extracted to calculate CMI, and the peak value is used to detect anomalies. After experiments, our algorithm is verified to be rapid and accurate in different scenarios.
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References
Sabzalian, B., Marvi, H., Ahmadyfard, A.: Deep and sparse features for anomaly detection and localization in video. In: 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA) (2019)
Qingsong, S., Chao, Z., Yu, C., et al.: Road segmentation using full convolutional neural networks with conditional random fields. J. Tsinghua Univ. (Sci. Technol.) 58, 725–731 (2018)
Tripathi, G., Singh, K., Vishwakarma, D.K.: Convolutional neural networks for crowd behaviour analysis: a survey. Vis. Comput. 35(5), 753–776 (2018)
Wei, Z.J., Qing, L.B., Wang, Z.Y., et al.: An abnormal behavior classification detection algorithm based on crowd density. Video Eng. 42(3), 97–102 (2018)
Liu, C., Wang, G., Ning, W., et al.: Anomaly detection in surveillance video using motion direction statistics. In: 2010 17th IEEE International Conference on Image Processing (ICIP). IEEE (2010)
Bertini, M., Bimbo, A.D., Seidenari, L.: Multi-scale and real-time non-parametric approach for anomaly detection and localization. Comput. Vis. Image Underst. 116(3), 320–329 (2012)
Peipei, Z., Qinghai, D., Haibo, L., Qinglin, H.: Anomaly detection and location in crowd surveillance videos. Acta Optica Sinica 38(08), 97–105 (2018)
Fanbo, L., Ping, W., Siyuan, L., et al.: Crowd abnormal behavior recognition based on deep learning and sparse optical flow. Comput. Eng. 46(04), 287–293+300 (2020)
Ying, C., Dandan, H.: Spatial-temporal stream anomaly detection based on Bayesian fusion. J. Electron. Inf. Technol. 41(05), 1137–1144 (2019)
Aguilar, W.G., Luna, M.A., Moya, J.F., et al.: Real-time detection and simulation of abnormal crowd behavior. In: International Conference on Augmented Reality (2017)
Rabiee, H., Mousavi, H., Nabi, M., Ravanbakhsh, M.: Detection and localization of crowd behavior using a novel tracklet-based model. Int. J. Mach. Learn. Cybern. 9(12), 1999–2010 (2017)
Feizi, A.: Hierarchical detection of abnormal behaviors in video surveillance through modeling normal behaviors based on AUC maximization. Soft. Comput. 24, 1–13 (2019)
Amraee, S., Vafaei, A., Jamshidi, K., Adibi, P.: Anomaly detection and localization in crowded scenes using connected component analysis. Multimed. Tools Appl. 77(12), 14767–14782 (2017)
Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 935–942 (2009)
Kim, J., Grauman, K.: Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2928 (2009)
Mahadevan, V., Li, W., et al.: Anomaly detection in crowded scenes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1975–1981 (2010)
Kangwei, L., Jianhua, W., Zhongzhi, H.: Abnormal event detection and localization using level set based on hybrid features. Signal Image Video Process. 12(2), 255–261 (2017)
Xu, D., Yan, Y., Ricci, E., et al.: Detecting anomalous events in videos by learning deep representations of appearance and motion. Comput. Vis. Image Underst. 156, 117–127 (2017)
Lee, D.G., Suk, H.I., Park, S.K., et al.: Motion influence map for unusual human activity detection and localization in crowded scenes. IEEE Trans. Circuits Syst. Video Technol. 25(10), 1 (2015)
Biswas, S., Babu, R.V.: Anomaly detection in compressed H.264/AVC video. Multimedia Tools and Applications 74(24), 11099–11115 (2014)
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Li, X., Yang, Y., Xu, Y., Li, L., Wang, C. (2021). Rapid Detection of Crowd Abnormal Behavior Based on the Hierarchical Thinking. In: Barolli, L., Li, K., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2020. Advances in Intelligent Systems and Computing, vol 1263. Springer, Cham. https://doi.org/10.1007/978-3-030-57796-4_35
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DOI: https://doi.org/10.1007/978-3-030-57796-4_35
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