Abstract
Real-time and accurate vehicle tracking by Cameras and Surveillance can provide strong support for the acquisition and application of important traffic parameters, which is the basis of the traffic condition evaluation and the reasonable traffic command and dispatch. To deal with difficult problems of vehicle tracking research in a complex environments, such as occlusion, sudden illumination change, similar target interference and real-time tracking, measures are taken as follows. Firstly, the existing color local entropy particle filter tracking method is improved. The symmetry of information entropy is used to overcome the tracking failure caused by large-area occlusion. Secondly, the SIFT feature tracking method is improved to enhance real-time performance and robustness. Thirdly, two tracking methods were combined according to their characteristics, aiming at effectively improving the quasi-determination and real-time performance of vehicle tracking. Fourthly, Kalman filter was used to predict the motion state of vehicles. According to the SIFT characteristics and license plate information of vehicles, the exact position of the lost target vehicles is quickly located. It has been verified by experiments that our method has effectively improved the accuracy and real-time performance of vehicle tracking in complex situations.
Highlights
In the modern road traffic environment, the use of computer vision technology to detect, track, and recognition vehicles in video surveillance has become the key application and research field of intelligent transportation system (ITS)
The yellow tracking box stands for improved color local entropy particle filter tracking algorithm (ICLEPF), the blue tracking box stands for the tracking effect of improved scale-invariant feature transform (SIFT) feature particle filtering tracking algorithm (ISIFTPF), and the red tracking box stands for the tracking effect of multifeature fusion particle filter (MFFPF)
(1) In order to deal with the tracking failure caused by large-area occlusion in the target tracking process, we propose an improved particle filter tracking algorithm based on color, which uses the symmetry of information entropy
Summary
In the modern road traffic environment, the use of computer vision technology to detect, track, and recognition vehicles in video surveillance has become the key application and research field of intelligent transportation system (ITS). In order to deal with the tracking failure caused by large-area occlusion in the target tracking process, an improved particle filter tracking algorithm based on color is proposed. This algorithm uses the symmetry of information. Wang et al.: Particle Filter Vehicles Tracking by Fusing Multiple Features entropy It solves problems of geometric distortion, partial occlusion and illumination change, it effectively mitigates the impact of large-area occlusion of vehicles. We take the Kalman filter to predict the motion state This helps to quickly search and locate for an accurate position of the lost target according to the SIFT and license plate characteristics of vehicles. Massive real-world experiments show that these methods can greatly improve the robustness and accuracy of tracking
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
