Correlation Filtering Target Tracking Based on Color and Part Spatial Relation Constraints

Since the model of the correlated filter trackers is sensitive to deformation and occlusion depending on the spatial layout of the object being tracked, the tracker drifts or even fails when the tracked object is deformed or partially obscured. Aiming at this problem, this paper proposes a tracking algorithm named Correlation Tracking Target Tracking Based on Color and Part Spatial Relation Constraints. The algorithm uses the global color statistical feature and target component sub-block feature of the target to be tracked, and the feature based on color statistics has strong robustness to the change of shape. The target component sub-block feature is used to jointly learn all parts of the correlation filter, and retain the structural information of the target object. The simulation results show that the improved algorithm can track the target more accurately when the target moves rapidly or occludes the target, such as the kernel correlation filter tracking algorithm and the correlation filter tracking algorithm with scale estimation, compared with many current tracking algorithms.