Research On Multi-moving Vehicle Tracking Method In Tunnel

With the development of China's transportation construction,China has the longest highway in the world,and it is also the country with the largest number of tunnels and bridges.Tunnels bring convenience to traffic and also put forward new requirements for traffic safety management and lighting control systems.In order to solve the over-illumination and ineffective lighting in the tunnel,we can adopt the lighting control system of “The light is on when The car goes into the tunnel and the light went out when the car left”.That is to say,this system adjusts the lighting level at any time according to whether there is a car passing through the tunnel or other information about the traffic condition.In general,the first step is to obtain the relevant information of the driving vehicle in the tunnel.To extract the relevant information of the driving vehicle,it is necessary to detect and track the vehicles on the road.With the development of computer vision and image processing technology,multi-target tracking technology has broad prospects and huge economic value in terms of safety monitoring,intelligent transportation and so on.In multi-target tracking technology,multi-target mutual occlusion and real-time tracking has become a hot topic of research.The particle filter algorithm as a nonlinear filtering method has been successfully applied to the target tracking problem.This article focuses on the multi-moving vehicle tracking technology based on particle filtering in the tunnel,mainly for two parts: moving target detection and multi-moving vehicle tracking.In moving target detection module,the influence of shadow on moving target detection is eliminated by using the improved code background modeling method,and the detection accuracy is improved.For multi-moving vehicle tracking,to solve the problems of standard particle filter algorithm Particle divergence and computational complexity caused by random sampling,this paper selects the particle sampling space based on the target detection region and obtains the target model of the kernel function weighted color feature HSV histogram.This method effectively inhibits particle diffusion and reduces computation.Due to the one-way driving environment of the tunnel lane,this paper proposes a weighted resampling algorithm based on the direction of movement,which improves the accuracy of the algorithm.Finally,this paper combines the color features and the local features SURF which are insensitive to light changes to comprehensively determine the target model,weakening the effects of illumination changes and the effects of close proximity and partial occlusion of similar colors on multi-moving vehicle tracking.Through example video experiments and data conclusions,it is shown that the improved method proposed in this paper has good robustness to the tracking of multi-moving vehicles in the tunnel.