Yali Zhao
ABSTRACT
Vehicle tracking is one of the most challenging tasks in the field of visual tracking. A vehicle tracking algorithm based on CNN is constructed to solve the problem of rapid movement, scale change and occlusion of vehicles in outdoor environment. The CNN is used to extract feature sets containing positive and negative samples. The output of the CNN is used as the input of the Logistics classifier to obtain the vehicle classifier, and the particle filter is used to track the target online. The experimental results show that the depth characteristics of CNN extraction can effectively distinguish between the target and the background, and combined with particle filtering algorithm for online tracking, it has high tracking accuracy and strong robustness. Compared with the existing tracking algorithms, the vehicle can be better tracked when faced with changes in lighting, vehicle occlusion, and scale changes.
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Index Terms
- Design and Implementation of Vehicle Tracking System Based on Depth Learning
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