Siamese block attention network for online update object tracking

Visual tracking is a crucial task in computer vision that concentrates on assessing the state of the target in each frame. Siamese based trackers possess an excellent tracking mechanism and balance the accuracy and efficiency well through continuous optimization of network structure. However, the rapid appearance variation and occlusion are still huge challenges to the accuracy and success rate of tracking task. Most Siamese based trackers rely on a fixed object template to match the target in search area and also neglect the importance of feature representation to tracking tasks. Based on the core idea of how to promote the recognition ability of trackers for the dynamic object and keep stable tracking during occlusion process, we present Siamese block attention network for online update object tracking referred to as SBAN. The proposed Siamese block attention module adopts a Siamese network structure to integrate two complementary global descriptors and establish the interdependence among channels to generate channel weights which can enhance the crucial features and restrain inessential ones. We also design an online update target module that can effectively utilize the history tracking information. The final updated target module is the integration of the given template, the process template, and the last tracking results. Experiments on four benchmarks, OTB2015, VOT2018, UAV123, LaSOT, illustrate that our tracker obtains outstanding tracking performance in accuracy and robustness while running at over 53 FPS.