Human action recognition is one of the core tasks in the field of computer aided driving. Considering that the auxiliary driving system requires high real-time, and the hardware requirements can not be too high, it is proposed to identify human behavior in a single image. Considering that the night illumination is insufficient, and the infrared camera receives the infrared radiation of the object, it can work at night without the influence of visible light. Therefore, we focus on the human behavior recognition in infrared images. According to the scale of the problem, we first use AlexNet with moderate network depth as the backbone network, then improve the network, modify the classification output layer of the network according to the classification number. After preprocessing the dataset to adapt to improve AlexNet, we trained and tested the network. The experimental results quantify the classification performance of the network. Experimental results show that the proposed algorithm mean average precision, average recall and F1 score are better than traditional methods.
Pedestrian detection in infrared images has been a hot and difficult research topic in computer version. Traditional methods of pedestrian detection mainly depend on the manual feature for the expression of human body and the results largely relies on the feature representation. Designing artificial features is time-consuming and labor intensive, requires heuristic expertise and experience. Deep learning model based on convolution neural network can automatically learn feature representation from the original images, while avoiding the drawbacks of artificial features. Its difficulty is the choice of network parameters. In this paper, we propose to use deep learning method based on convolution neural network in the process of pedestrian detection. In addition, we analyze the impact of network layers, convolution kernel sizes and feature maps to pedestrian detection in infrared images. The results demonstrate the superiority of our method over traditional methods in detection rate and alarm rate.
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