Learning Localization-Aware Target Confidence for Siamese Visual Tracking

Siamese tracking paradigm has achieved great success, providing effective appearance discrimination and size estimation by classification and regression. While such a paradigm typically optimizes the classification and regression independently, leading to task misalignment (accurate prediction boxes have no high target confidence scores). In this paper, to alleviate this misalignment, we propose a novel tracking paradigm, called SiamLA. Within this paradigm, a series of simple, yet effective localization-aware components are introduced to generate localization-aware target confidence scores. Specifically, with the proposed
localization-aware dynamic label
(LADL) loss and
localization-aware label smoothing
(LALS) strategy, collaborative optimization between the classification and regression is achieved, enabling classification scores to be aware of location state, not just appearance similarity. Besides, we propose a separate
localization-aware quality prediction
(LAQP) branch to produce location quality scores to further modify the classification scores. To guide a more reliable modification, a novel
localization-aware feature aggregation
(LAFA) module is designed and embedded into this branch. Consequently, the resulting target confidence scores are more discriminative for the location state, allowing accurate prediction boxes tend to be predicted as high scores. Extensive experiments are conducted on six challenging benchmarks, including GOT-10 k, TrackingNet, LaSOT, TNL2K, OTB100 and VOT2018. Our SiamLA achieves competitive performance in terms of both accuracy and efficiency. Furthermore, a stability analysis reveals that our tracking paradigm is relatively stable, implying that the paradigm is potential for real-world applications.