Aiming at the high complexity of existing crowd abnormal detection models, the inability of traditional CNN to extract time-related features, and the lack of training samples, an improved spatial-temporal convolution neural network is proposed in this paper. The algorithm firstly uses the aggregation channel feature model to process the surveillance image, and selects the suspected object region with saliency characteristics. Then, the scaled correction and feature extraction are performed on the obtained suspected object region. The corresponding low-level features are obtained and input into the deep network for deep feature learning so as to enhance the representation ability. Finally, the deep feature is input into the least squares SVM classification model to obtain the final abnormal behavior detection result. The embedded chip Hi353I is used as the hardware processor to realize the real-time abnormal behavior detection effect. Our proposed deep intelligent analysis algorithm is used as abnormal Behavior Detector in the board level test. The results show that most of abnormal behaviors can be detected and the alarming message can be timely transmitted in the real-time surveillance.
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Li, C., Han, Z., Ye, Q., et al.: Visual abnormal behavior detection based on trajectory sparse reconstruction analysis[J]. Neurocomputing. 119(16), 94–100 (2013)
Ko, K.E., Sim, K.B.: Deep convolutional framework for abnormal behavior detection in a smart surveillance system[J]. Eng. Appl. Artif. Intell. 67, 226–234 (2018)
Xiong, G., Cheng, J., Wu, X., et al.: An energy model approach to people counting for abnormal crowd behavior detection[J]. Neurocomputing. 83(23), 121–135 (2012)
Devroye, L., Wise, G.L.: Detection of abnormal behavior via nonparametric estimation of the support[J]. SIAM J. Appl. Math. 38(3), 480–488 (1980)
Zhang, J., Wu, C., Wang, Y., et al.: Detection of abnormal behavior in narrow scene with perspective distortion[J]. Mach. Vis. Appl. 1–12 (2018)
Rasheed, N., Khan, S.A., Khalid, A., et al.: Tracking and abnormal behavior detection in video surveillance using optical flow and neural networks[C]. 28th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2014. IEEE Computer Society. 2014:61–66
Cheng G, Wang S, Guo T, et al. Abnormal behavior detection for harbour operator safety under complex video surveillance scenes[C]. 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). 2018:28–33
Jeong, H., Chang, H.J., Choi, J.Y.: Modeling of moving object trajectory by spatio-temporal learning for abnormal behavior detection[C]// IEEE International Conference on Advanced Video & Signal Based Surveillance. IEEE Comput. Soc. 71–82 (2011)
Wang, Q., Ma, Q., Luo, C.H., et al.: Hybrid histogram of oriented optical flow for abnormal behavior detection in crowd scenes[J]. Int. J. Pattern Recognit. Artif. Intell. 30(02), 14–23 (2016)
Sabokrou, M., et al.: Deep-anomaly: fully convolutional neural network for fast anomaly detection in crowded scenes. Comput. Vis. Image Underst. 172, 88–97 (2018)
Bouttefroy P L M, Bouzerdoum A, Phung S L, et al. Local estimation of displacement density for abnormal behavior detection[C]. IEEE Workshop on Machine Learning for Signal Processing. 2008:29–2036
Lee J J, Kim G J, Kim M H. Trajectory extraction for abnormal behavior detection in public area[C]. International Conference & Expo on Emerging Technologies for A Smarter World. 2013:212–218
Li C L, Hao Z B, Li J J. Abnormal behavior detection using a novel behavior representation[C]. International Conference on Apperceiving Computing & Intelligence Analysis. 2011:54–60
Xiang J, Fan H, Xu J, et al. Abnormal behavior detection based on spatial-temporal features[C]. International Conference on Machine Learning And Cybernetics, 2013: 871–876
Avinash, R., Vinod, P.: Tucker tensor decomposition-based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videos[J]. IET Comput. Vis. 12(6), 933–940 (2018)
Hirsch, M., et al.: A two-phase energy-aware scheduling approach for CPU-intensive jobs in mobile grids. J. Grid Comput. 15.1, 1–26 (2017)
Xia, K.-j., Yin, H.-s., Zhang, Y.-d.: Deep semantic segmentation of kidney and space-occupying lesion area based on SCNN and ResNet models combined with SIFT-Flow algorithm. J. Med. Syst. 43, 2 (2019). https://doi.org/10.1007/s10916-018-1116-1
Xia, K.J., Yin, H.S., Wang, J.Q.: A novel improved deep convolutional neural network model for medical image fusion [J]. Clust. Comput. 3, 1–13 (2018)
Qianyin J, Guoming L, Jinwei Y, et al. A model based method of pedestrian abnormal behavior detection in traffic scene[C]. IEEE International Smart Cities Conference, 2015: 1–6
Rezazadegan F, Shirazi S, Upcrofit B, et al. Action recognition: From static datasets to moving robots[C]. 2017 IEEE International Conference on Robotics and Automation (ICRA). 2017:3185–3191
Xia, K., Yin, H., Qian, P., Jiang, Y., Wang, S.: Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images. IEEE Access. 7, 96349–96358 (2019)
Qian, P., Xi, C., Xu, M., Jiang, Y., Kuan-Hao, S., Wang, S., Muzic Jr., R.F.: SSC-EKE: semi-supervised classification with extensive knowledge exploitation. Inf. Sci. 422, 51–76 (2018)
Qian, P., Sun, S., Jiang, Y., Kuan-Hao, S., Ni, T., Wang, S., Muzic Jr., R.F.: Cross-domain, soft-partition clustering with diversity measure and knowledge reference. Pattern Recogn. 50, 155–177 (2016)
Kajian, X.I.A., Jiangqiang, W.A.N.G., Yue, W.U.: Robust Alzheimer Disease classification based on Feature Integration Fusion Model for Magnetic[J]. J. Med. Imag. Health Inf. 7, 1–6 (2017)
Fang, W., Beckert, U.: Parallel tree search in volunteer computing: a case study. J. Grid Comput. 4, 1–16 (2017)
Rui, Y., Bing, L., Ye-Lin, H., et al.: A method for abnormal behavior recognition based on deep learning[J]. Journal of Wuyi University (Natural Science Edition). 12(2), 112–122 (2018)
Fang, Z., Fei, F., Fang, Y., et al.: Abnormal event detection in crowded scenes based on deep learning[J]. Multimed. Tools Appl. 75(22), 14617–14639 (2016)
Kovács, J., Kacsuk, P.: Occopus: a multi-cloud orchestrator to deploy and manage complex scientific infrastructures[J]. J. Grid Comput. 16(1), 1–19 (2017)
Qian, P., Zhou, J., Jiang, Y., Liang, F., Zhao, K., Wang, S., Kuan-Hao, S., Muzic Jr., R.F.: Multi-view maximum entropy clustering by jointly leveraging inter-view collaborations and intra-view-weighted attributes. IEEE Access. 6, 28594–28610 (2018)
Ramon-Cortes, C., et al.: Transparent orchestration of task-based parallel applications in containers platforms. J. Grid Comput. 16.1, 137–160 (2018)
Tighe, M., Bauer, M.: Topology and application aware dynamic VM Management in the Cloud. J. Grid Comput. 4, 1–22 (2017)
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Hu, Y. Design and Implementation of Abnormal Behavior Detection Based on Deep Intelligent Analysis Algorithms in Massive Video Surveillance. J Grid Computing 18, 227–237 (2020). https://doi.org/10.1007/s10723-020-09506-2
- Abnormal behavior detection
- Deep learning
- Spatial-temporal convolution
- Embedded platform
- Aggregate channel feature