Video Tracking Algorithm Based on Particle Filter and Online Random Forest

摘要

Because Particle Filter algorithm in video tracking has some problems, such as weight degradation, poor samples after resampling, being difficult to select the optimal important probability density, being difficult to accurately follow to the target again after tracking failure, the random forest learning algorithm is introduced into the Particle Filter tracking algorithm. The current estimated target state image is transmitted to the random forest detection module, the algorithm adopts reliable sample update strategy, the random forest model is used to detect the target area, and the decision tree detection module rapidly detects target state adjacent areas. In the case of target temporarily lost, two methods of collaboration eventually accurately allocate to the tracking target. The algorithm ensures the robustness of tracking, effectively avoids the drift problem.