Title: Fighting behaviour detection in video using convolutional neural network

Authors: Ying Huang; Ling Lai

Addresses: School of Mathematics and Computer Science, Gannan Normal University, Ganzhou City, Jiangxi Province, China ' School of Mathematics and Computer Science, Gannan Normal University, Ganzhou City, Jiangxi Province, China

Abstract: With the rapid development of computer networks and artificial intelligence technology, video surveillance technology has developed rapidly and tends to be intelligent. This paper aims to use deep learning technology to achieve abnormal detection of fighting behaviour in specific video surveillance scenarios, and then analyse and process more complex human gesture behaviour recognition problem. The collected initial video data was well handled with data pre-processed and enhancement, the corresponding model was also further optimised. Experimental results show that the improved convolutional neural network model accuracy rate reached 92.53%, while using migration technology to quote classic. The convolutional neural network structure VGG16 and GoogleNet model can reach 98.71% and 99.60% accuracy.

Keywords: fighting behaviour detection; deep learning; convolutional neural network; transfer learning; video analysis.

DOI: 10.1504/IJWMC.2021.119974

International Journal of Wireless and Mobile Computing, 2021 Vol.21 No.2, pp.101 - 112

Received: 24 Oct 2020
Accepted: 28 Jan 2021
Published online: 04 Jan 2022 *

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