Learning Multifeature Correlation Filter and Saliency Redetection for Long-Term Object Tracking

Abstract

Recently due to the good balance between performance and tracking speed, the discriminative correlation filter (DCF) has become a popular and excellent tracking method in short-term tracking. Computing the correlation of a response map can be efficiently performed in the Fourier domain by the discrete Fourier transform (DFT) of the input, where the DFT of an image has symmetry in the Fourier domain. However, most of the correlation filter (CF)-based trackers cannot deal with the tracking results and lack the effective mechanism to adjust the tracked errors during the tracking process, thus usually perform poorly in long-term tracking. In this paper, we propose a long-term tracking framework, which includes a tracking-by-detection part and redetection part. The tracking-by-detection part is built on a DCF framework, by integrating with a multifeature fusion model, which can effectively improve the discriminant ability of the correlation filter for some challenging situations, such as occlusion and color change. The redetection part can search the tracked object in a larger region and refine the tracking results after the tracking has failed. Benefited by the proposed redetection strategy, the tracking results are re-evaluated and refined, if it is necessary, in each frame. Moreover, the reliable estimation module in the redetection part can effectively identify whether the tracking results are correct and determine whether the redetector needs to open. The proposed redetection part utilizes a saliency detection algorithm, which is fast and valid for object detection in a limited region. These two parts can be integrated into DCF-based tracking methods to improve the long-term tracking performance and robustness. Extensive experiments on OTB2015 and VOT2016 benchmarks show that our proposed long-term tracking method has a proven effectiveness and high efficiency compared with various tracking methods.