Design and Implementation of a Lightweight Online Human Behavior Recognition Method for Nighttime Surveillance Videos

This paper aims to study human behavior recognition techniques in nighttime near-infrared surveillance videos and address the lack of lightweight behavior recognition networks in the field of nighttime near-infrared public monitoring. The goal is to develop a method suitable for lightweight edge processing devices. Due to the absence of a nighttime infrared video behavior recognition dataset, we have constructed a custom dataset for human behavior in near-infrared monitoring. This dataset involved 1630 video samples categorized into 10 classes. To overcome the challenge posed by large models and high device requirements, we propose a behavior recognition algorithm based on lightweight two-dimensional convolutional neural networks. This algorithm can adapt to low-quality near-infrared surveillance videos. Besides, it's able to focus on different temporal features and avoid interference from static background frames in long videos. The proposed method achieves an accuracy of 92.3% by using the self-built dataset captured by an active infrared camera and exhibits a processing speed of 35ms per video on the AGX Xavier device. Compared to popular lightweight algorithms like MoViNet-A0 and X3D-XS, this algorithm achieves higher accuracy and shorter processing time under similar model computational complexity.