At present, academic research mainly focuses on detecting driver fatigue and distraction through the driver's eyes and head. But there are few studies on detecting driving behavior through the head, hands and even the body, most of which use the skin color detection method to extract a single full-image pixel as a feature and the dimension is too large, problems such as instantaneous region overlap and partial occlusion occur inevitably in the detection process, thereby affecting the detection accuracy. In this paper, we propose a driving posture detection method based on video and skin color region distance. The image features are represented by extracting the skin color region centroid coordinates of the sampled images from videos and converting them into feature distances. Then the BP neural network is used to implement the identification and classification of driving behavior, which can effectively improve the detection rate of the driving behavior, and finally realize the real-time warning of the driving process.
In order to identify the status of traffic lights in urban traffic scenes effectively, a recognition method of traffic lights using HSV color space model is proposed in this paper. Firstly, the median filter and the light compensation algorithm are used to preprocess images of urban traffic scenes. Secondly, the template matching method of traffic lights and the Bhattacharyya coefficient are used to detection of the traffic lights area in images of traffic scenes. Finally, the status of traffic lights in urban traffic scenes are identified using HSV color space model. The experimental results show that the proposed recognition method of traffic lights using HSV color space model offers the best performance than RGB color space model and YCbCr color space model. The recognition accuracies of red, green and yellow traffic lights are 96.67%, 95.0% and 88.67%, respectively.