Long-term object tracking algorithm with occlusion-awareness and re-detection

To solve the problem of target loss as occlusion for a variety of Correlation Filter based trackers, an improved tracking algorithm is proposed based on occlusion awareness and target re-detection mechanism in this paper, in which the occlusion awareness module is used to evaluate whether the tracked object is occluded or whether the tracking result is reliable. As the events as occlusion that results in tracking failure occur, the object re-detection module is triggered to redetect the original tracking target based on integral map of pixel-wise object confidence from color information. Furthermore, when the tracking quality is unreliable and no reliable object is re-detected, and the tracking model is not updated. Experiments show that the proposed algorithm can effectively avoid the problem of the Correlation Filter tracker’s variants, loss of the tracked object and model drift caused by occlusion, its tracking performance is obviously improved compared with that of several state-of-the-arts Correlation Filter tracker’s variants.