Min-Jeong Kim¹, Byeong-Uk Jeon¹, Hyun Yoo², Kyungyong Chung^3,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2371-2386, 2023, DOI:10.32604/iasc.2023.040310 - 21 June 2023

Abstract With the increasing number of digital devices generating a vast amount of video data, the recognition of abnormal image patterns has become more important. Accordingly, it is necessary to develop a method that achieves this task using object and behavior information within video data. Existing methods for detecting abnormal behaviors only focus on simple motions, therefore they cannot determine the overall behavior occurring throughout a video. In this study, an abnormal behavior detection method that uses deep learning (DL)-based video-data structuring is proposed. Objects and motions are first extracted from continuous images by combining existing More >

Huifang Qian¹, Xuan Zhou^{1, *}, Mengmeng Zheng¹

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2153-2167, 2020, DOI:10.32604/cmc.2020.011843 - 16 September 2020

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… More >