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Trackers based on Siamese network have shown tremendous success, because oftheir balance between accuracy and speed. Nevertheless, with tracking scenariosbecoming more and more sophisticated, most existing Siamese-based approachesignore the addressing of the problem that distinguishes the tracking targetfrom hard negative samples in the tracking phase. The features learned by thesenetworks lack of discrimination, which significantly weakens the robustness ofSiamese-based trackers and leads to suboptimal performance. To address thisissue, we propose a simple yet efficient hard negative samples emphasis method,which constrains Siamese network to learn features that are aware of hardnegative samples and enhance the discrimination of embedding features. Througha distance constraint, we force to shorten the distance between exemplar vectorand positive vectors, meanwhile, enlarge the distance between exemplar vectorand hard negative vectors. Furthermore, we explore a novel anchor-free trackingframework in a per-pixel prediction fashion, which can significantly reduce thenumber of hyper-parameters and simplify the tracking process by taking fulladvantage of the representation of convolutional neural network. Extensiveexperiments on six standard benchmark datasets demonstrate that the proposedmethod can perform favorable results against state-of-the-art approaches.

Hard Negative Samples Emphasis Tracker without Anchors