2016/12/16 by Baochang Zhang, Zhang, Baochang, Zhigang Li +13
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Image Enhancement Techniques #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1612.05365
arXiv admin note: text overlap with arXiv:1404.7584 by other authors
arxiv created 2016/12/16 · openalex publication_date 2016/12/16 · arxiv updated 2016/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Kernelized Correlation Filter (KCF) is one of the state-of-the-art object trackers. However, it does not reasonably model the distribution of correlation response during tracking process, which might cause the drifting problem, especially when targets undergo significant appearance changes due to occlusion, camera shaking, and/or deformation. In this paper, we propose an Output Constraint Transfer (OCT) method that by modeling the distribution of correlation response in a Bayesian optimization framework is able to mitigate the drifting problem. OCT builds upon the reasonable assumption that the correlation response to the target image follows a Gaussian distribution, which we exploit to select training samples and reduce model uncertainty. OCT is rooted in a new theory which transfers data distribution to a constraint of the optimized variable, leading to an efficient framework to calculate correlation filters. Extensive experiments on a commonly used tracking benchmark show that the proposed method significantly improves KCF, and achieves better performance than other state-of-the-art trackers. To encourage further developments, the source code is made available https://github.com/bczhangbczhang/OCT-KCF.