  Siamese tracking paradigm has achieved great success, providing effective
appearance discrimination and size estimation by the 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 branch, centered on a
localization-aware feature aggregation (LAFA) module, to produce location
quality scores to further modify the classification scores. 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-10k, TrackingNet, LaSOT, TNL2K, OTB100 and VOT2018. Our SiamLA achieves
state-of-the-art performance in terms of both accuracy and efficiency.
Furthermore, a stability analysis reveals that our tracking paradigm is
relatively stable, implying the paradigm is potential to real-world
applications.
