@Article{cmc.2020.011843,
AUTHOR = {Huifang Qian, Xuan Zhou, Mengmeng Zheng},
TITLE = {Abnormal Behavior Detection and Recognition Method Based on  Improved ResNet Model},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2153--2167},
URL = {http://www.techscience.com/cmc/v65n3/40161},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2020.011843}
}