Trucker Behavior Security Surveillance Based on Human Parsing

With the increase of roads and vehicles, the number of traffic accidents has gradually increased, and one of the important reasons is the driver's unsafe driving behavior. Some existing methods rely on the method of joint point estimation to identify driving behavior. This kind of method can filter out unrelated components such as background, but the joint point estimation can only give the position information of human behavior, which cannot fully describe the human body, so we try to solve this problem. For one thing, we propose a driver behavior recognition method based on human parsing which can quickly and accurately recognize the driver's unsafe behavior. For another thing, we build a trucker behavior dataset (TB dataset) with 700 videos and improve a human body segmentation model with higher precision. Finally, we achieve 93.39% and 91.32% accurate for recognizing driving with one-hand and no-hand. At the same time, we get 98.75% accuracy for recognizing driving with turned head by using ResNet50 model.