Robust Tracking and Redetection: Collaboratively Modeling the Target and Its Context

Abstract

Robust object tracking and redetection require stably predicting the trajectory of the target object and recovering from tracking failure by quickly redetecting it when it is lost during long-term tracking. The locations of the target and the background are calculated relative to the region occupied by the object. The effect of tracking can be enhanced by isolating the target and the background, modeling and tracking them, respectively, and integrating their tracking results. In this study, we propose an approach that builds motion models for the target and its context. Tracking results from a target tracker and a context tracker are integrated through linear fusion to predict the position of the target. A kernelized correlation filter tracker is used to track the target in the predicted position. When the target is lost, it can be quickly recovered by searching in the given field of view using a target model built and updated through observation models that are constructed prior to the loss of the target. Our approach is not sensitive to the segmentation of the target and the context. The motion models and observation models of the target and the context work together in the tracking process, whereas the target model alone is involved in redetection. Experiments to test our proposed approach, which simultaneously models the target and its context, showed that it can effectively enhance the robustness of long-term tracking.