Complementary Tracking via Dual Color Clustering and Spatio-Temporal Regularized Correlation Learning

Recently, a simple, yet effective and efficient tracker named Staple has achieved promising performance in terms of efficiency and accuracy on a series of visual tracking benchmarks. Staple is equipped with complementary learners of discriminative correlation filters (DCFs) and color histograms, which are robust to both color changes and deformations. However, it has some drawbacks: 1) Staple only employs standard color histograms with the same quantization step for all sequences, which does not consider the specific structural information of target in each sequence, thereby affecting its discriminative capability to separate target from background. 2) The standard DCFs are efficient but suffer from unwanted boundary effects, leading to failures in some challenging scenarios. To address these issues, we present a dual color clustering and spatio-temporal regularized correlation regressions-based complementary tracker (CSCT). The proposed CSCT includes two components with complementary merits to adaptively deal with significant color variations and deformations for each sequence: First, we design a novel color clustering-based histogram model that first adaptively divides the colors of the target in the 1st frame into several cluster centers, and then the cluster centers are taken as references to construct adaptive color histograms for targets in the coming frames, which enable to adapt significant target deformations. Second, we propose to learn spatio-temporal regularized CFs, which not only enable to avoid boundary effects but also provides a more robust appearance model than the discriminative CFs in Staple in the case of large appearance variations. Compared to Staple, our CSCT with handcrafted features achieves a gain of 5.9%, 3.4%, and 1.5% on OTB100, Temple-Color, and VOT2016 benchmarks in terms of AUC and EAO scores, respectively. Moreover, our CSCT performs favorably against several state-of-the-art trackers, including the deep learning-based trackers.