2016/09/20 by Martin Danelljan, G Hager, Gustav Häger +6
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Image Enhancement Techniques #Video Surveillance and Tracking Methods #Visual Attention and Saliency Detection #cs.CV
paper · pdf · doi:10.48550/arxiv.1609.06118
CVPR 2016
arxiv created 2016/09/20 · openalex publication_date 2016/09/20 · arxiv updated 2016/09/21 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker itself. This often leads to the inclusion of corrupted training samples, due to occlusions, misalignments and other perturbations. Existing tracking-by-detection methods either ignore this problem, or employ a separate component for managing the training set. We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks. Our approach dynamically manages the training set by estimating the quality of the samples. Contrary to existing approaches, we propose a unified formulation by minimizing a single loss over both the target appearance model and the sample quality weights. The joint formulation enables corrupted samples to be down-weighted while increasing the impact of correct ones. Experiments are performed on three benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color with 128 videos. On the OTB-2015, our unified formulation significantly improves the baseline, with a gain of 3.8% in mean overlap precision. Finally, our method achieves state-of-the-art results on all three datasets. Code and supplementary material are available at http://www.cvl.isy.liu.se/research/objrec/visualtracking/decontrack/index.html .