Flow Guided Short-term Trackers with Cascade Detection for Long-term Tracking
This addresses the problem of target loss in long sequences for tracking applications, but it appears incremental as it builds upon existing short-term methods.
They tackled long-term object tracking where targets disappear due to occlusion or leaving view, proposing a novel algorithm that adds a tracking result judgement module and detection module to short-term trackers, resulting in effectiveness in handling target disappearance.
Object tracking has been studied for decades, but most of the existing works are focused on the short-term tracking. For a long sequence, the object is often fully occluded or out of view for a long time, and existing short-term object tracking algorithms often lose the target, and it is difficult to re-catch the target even if it reappears again. In this paper a novel long-term object tracking algorithm flow_MDNet_RPN is proposed, in which a tracking result judgement module and a detection module are added to the short-term object tracking algorithm. Experiments show that the proposed long-term tracking algorithm is effective to the problem of target disappearance.