Discriminative collaborative representation-based tracking

Xiaohuan Lu, Yingyi Liang, Zhenyu He

Abstract: Recently, sparse representation has been widely introduced into tracking methods to improve their performance. However, these methods only focus on reconstructing the candidate samples while ignoring the discriminative information of the background, which greatly limits their performance, especially when the target undergoes heavy occlusion. To tackle this issue, we propose a discriminative collaborative representation-based tracker. We first propose an appearance model based on collaborative representation, in which the appearance of a candidate is represented as a linear combination of the dictionary with a discriminative constraint in the training stage. This constraint can enlarge the margins of reconstruction coefficients that correspond to the positive and negative templates of dictionary. To further enhance the discriminability of the tracker, we introduce this constraint to a state estimation model in the decision stage, which utilizes the reconstruction coefficients to search the optimal candidate as the tracking result. In addition, we use a dictionary update strategy based on collaborative representation, which can promote the adaptability of the tracker. This strategy not only facilitates the dictionary to preserve the historical appearance of the tracking object but also prevents the seriously occluded target from being introduced into dictionary. The experimental results on several challenging sequences demonstrate the robustness of our tracker.