关键词: 车辆碰撞, 灰度空间, 间隙特征向量, 分割, 优化, 分类和计数

Abstract: Due to the influence of shape, illumination, visual collisions and visual blur, vehicle classification and counting are complex problems in video-based surveillance. Specifically, there is an indispensable part that should be handled carefully, i.e., the foreground extraction. In the initial foreground extraction, a model is established to determine whether there is a vehicle bonding. For the vehicles with visual collision, the model candetermine more accurate foregrounds through the gray space double threshold and the YCbCr image space processing. For such purpose, the paper defines the gap feature vector to recast the vehicle segmentation problem into the optimization problem of finding the dividing points, and gives an efficient vehicle segmentation algorithm to segment the vehicle. Final, a deep neural network is employed to classify the segmented vehicles. The experimental results show that our model is in a position to handle the surveillance videos in practice. Compared with manually calculating vehicle traffic or establishing a three-dimensional model to analyze vehicle classification and counting in the case of vehicle collisions, the proposed method takes the accuracy and timeliness into account. The efficiency is improved but the cost is reduced.

Key words: vehicle bonding, gray space, gap feature vector, segmentation, optimization, classification and counting