Vol.65, No.3, 2020, pp.2153-2167, doi:10.32604/cmc.2020.011843
Abnormal Behavior Detection and Recognition Method Based on Improved ResNet Model
  • Huifang Qian1, Xuan Zhou1, *, Mengmeng Zheng1
1 School of Electronics and Information, Xi’an Polytechnic University, Xi’an, 710048, China.
* Corresponding Author: Xuan Zhou. Email: zhou_xuan668@163.com.
Received 01 June 2020; Accepted 25 June 2020; Issue published 16 September 2020
The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately. The key breakthrough point in recognizing abnormal behaviors is how to obtain the effective features of the picture, so as to solve the problem of recognizing them. In response to this difficulty, this paper introduces an adjustable jump link coefficients model based on the residual network. The effective coefficients for each layer of the network can be set after using this model to further improving the recognition accuracy of abnormal behavior. A convolution kernel of 1×1 size is added to reduce the number of parameters for the purpose of improving the speed of the model in this paper. In order to reduce the noise of the data edge, and at the same time, improve the accuracy of the data and speed up the training, a BN (Batch Normalization) layer is added before the activation function in this network. This paper trains this network model on the public ImageNet dataset, and then uses the transfer learning method to recognize these abnormal behaviors of human in the UTI behavior dataset processed by the YOLO_v3 target detection network. Under the same experimental conditions, compared with the original ResNet-50 model, the improved model in this paper has a 2.8% higher accuracy in recognition of abnormal behaviors on the public UTI dataset.
ResNet, abnormal behavior recognition, YOLO_v3, adjustable jump link coefficients model, standard normal distribution.
Cite This Article
Qian, H., Zhou, X., Zheng, M. (2020). Abnormal Behavior Detection and Recognition Method Based on Improved ResNet Model. CMC-Computers, Materials & Continua, 65(3), 2153–2167.
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