Object Tracking via Temporal Consistency Dictionary Learning

Sparse representation-based methods have been successfully applied to visual tracking. However, complex and inefficient optimization limits their deployment in practical tracking scenarios. In this paper, we propose a temporal consistency dictionary learning tracking algorithm to enable efficient dictionary learning and tracking executive. First, we present an objective function which introduces the fixed dictionary and variance dictionary to reconstruct the object’s appearance. In particular, the proposed method takes the temporal consistency into account by adding a regularization term into the objective function to constrain the difference of object appearance at adjacent frames. Then the optimization problem is solved in an iteration way. Moreover, the proposed method can encode the object’s local structural information, and the local patches from the same candidate altogether for a global appearance representation. Second, we develop an effective observation likelihood function based on the proposed model. It takes the influence of patches with large reconstruction errors into consideration, thereby, alleviating the drifting of the object. Finally, we present an appearance updating strategy to adapt to the object’s appearance variations by the online dictionary learning. Experimental evaluations on the TB50 and TB100 datasets show that the proposed tracking method outperforms sparse representation related visual tracking as well as other state-of-the-art tracking methods.