A Weighted Particle Filter For Pedestrian Tracking in Complex Scenarios

Focusing on the problem that traditional particle filter tracking algorithm prone to drift and tracking accuracy is unsatisfactory when there is some interference from shading on the target, the light or which background is similar to the pedestrian, a weighted particle filter for pedestrian tracking method is proposed. This method combines occlusion model and Online Boosting algorithm to reconstruct the particle weights, using online learning update strong classifiers in real time, meanwhile, combined with several impact factors, such as occlusion model, the distance and the similarity between last time target location and current location, to realize pedestrian adaptive tracking in the complex scenarios. Experiment results on PETS - L2S1 public data and my own data set show that the proposed method can effectively remove the interference from object shelter, similar backgrounds and light mutation, the weighted particle filter method could accomplish pedestrian tracking stably, accurately and in real time.