The moving target tracking and segmentation method based on space-time fusion

Abstract

At present, the target tracking method based on the correlation operation mainly uses deep learning to extract spatial information from video frames and then performs correlations on this basis. However, it does not extract the motion features of tracking targets on the time axis, and thus tracked targets can be easily lost when occlusion occurs. To this end, a spatiotemporal motion target tracking model incorporating Kalman filtering is proposed with the aim of alleviating the problem of occlusion in the tracking process. In combination with the segmentation model, a suitable model is selected by scores to predict or detect the current state of the target. We use an elliptic fitting strategy to evaluate the bounding boxes online. Experiments demonstrate that our approach performs well and is stable in the face of multiple challenges (such as occlusion) on the VOT2016 and VOT2018 datasets with guaranteed real-time algorithm performance.