Discriminative Siamese Complementary Tracker With Flexible Update

Abstract

The offline generative Siamese trackers are equipped with the pre-defined anchors and the fixed target template. They overlook the target-background discriminative information, and lack the flexible target-specific update strategy. To overcome above drawbacks, we propose an adaptive and discriminative Siamese complementary tracking network with flexible update scheme. It consists of three collaborate subnetworks: anchor-free Siamese attention classification and regression subnetwork, online discriminative learning with multi-attention and multi-peak suppression, classifier guided template update subnetwork. All of them are interdependent and complementary to enhance each other for accurate target location. Specifically, an anchor-free multi-attention Siamese tracking subnetwork directly classifies the corresponding image patches with reliability assessment, and cascaded regresses the bounding boxes to progressively refine the predicting accuracy. Its evaluation is flexible and general with both proposal and anchor free in per-pixel prediction manner. Then, we integrate an online discriminative classifier optimizing module as a complementary subnetwork. It introduces spatial-temporal attention mechanism to fully explore multi-view multi-scale target-specific features, and evaluates multi-peak suppression to obtain a single centered peak response map. Its classified results can be fused with Siamese classification branch for accurate target location. Finally, the template update subnetwork is guided by the online discriminative classification scores. Extensive experiments on recent tracking datasets verify its top-ranked tracking accuracy and robustness against some state-of-the-art trackers.