Anomaly detection and reconciliation of pedestrian tracking trajectory

Pedestrian tracking plays an essential role in the domain of visual tracking. Much research in recent years has focused on how to obtain the accurate tracking results. However, few researchers have addressed the problem of the smoothness for the tracking trajectory. Most trajectory results are skipped and lack of smoothness, which does not comply with human vision habits. Also, some incorrect data has been recorded in the final trajectory due to the problem of partial occlusion. In this paper, we proposed a strategy to fix the present tracking trajectory. First, we detect and delete the anomaly tracking data. Second, we use a technique of supervised learning to do the linear regression for reconciling the tracking trajectory. The experiment manifests that the performance of the tracking trajectory is more accurate and stable than the original one after the implementation of our proposed strategy.