文献类型：期刊文献

英文题名：Moving Vehicle Tracking Optimization Method Based on SPF

作者：Lv, Caixia[1,2]; Zhang, Xuejing[2]

第一作者：Lv, Caixia

机构：[1] School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China; [2] Smart City College, Beijing Union University, Beijing, 100101, China

第一机构：School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

通讯机构：[1]School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

年份：2020

卷号：2020

外文期刊名：Complexity

收录：EI(收录号：20204809546906);Scopus(收录号：2-s2.0-85096568071)

语种：英文

外文关键词：Crime - Graph theory - Intelligent systems - Particle size analysis - Target tracking

摘要：In the intelligent transportation system, the license information can be automatically recognized by the computer and the vehicle can be tracked. Red light running, illegal change of lanes, vehicle retrograde, and other illegal driving events are reasonably recorded. This is undoubtedly an effective help for the traffic police to relieve the huge work pressure. However, in China, a considerable number of vehicle tracking methods have certain limitations in resisting complex external environmental influences. The external environmental factors include but not limited to variable factors such as camera movement, jitter, and severe rain and snow. These factors cannot be controlled well, so the tracking accuracy is greatly reduced. In regard to this, this paper proposes an optimization method for moving vehicle tracking based on SPF. First, according to the size of the overlapping area of the motion area between the two images, the researcher can construct and simplify the vertex adjacency matrix that reflects the characteristics of the undirected bipartite graph. Then according to the corresponding relationship between the vertex adjacency matrix and the regional behavior and vehicle behavior, the researcher completes the regional behavior analysis and vehicle behavior analysis. On this basis, a particle filter vehicle tracking algorithm based on segmentation compensation is introduced, and the vector sum of the tracked segmentation area is used as the final position of the target vehicle. In this way, as many scattered particles fall on the target area as possible, which will greatly improve the efficiency of particle utilization, enhance tracking accuracy, and avoid the problem of tracking failure caused by too fast vehicle movement. Through experimental simulation, it can be seen that the method proposed in this paper can greatly enhance the vehicle tracking ability when tracking vehicles in "complex environments." ? 2020 Caixia Lv and Xuejing Zhang.