Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling
arXiv:1811.07498 · doi:10.1109/TCSVT.2018.2882339
Abstract
It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model. Motivated by this idea, we propose a novel correlation filter-based tracker in this work, in which the temporal relatedness is reconciled under a multi-task learning framework and the multiple feature cues are modeled using a multi-view learning approach. We demonstrate the resulting regression model can be efficiently learned by exploiting the structure of blockwise diagonal matrix. A fast blockwise diagonal matrix inversion algorithm is developed thereafter for efficient online tracking. Meanwhile, we incorporate an adaptive scale estimation mechanism to strengthen the stability of scale variation tracking. We implement our tracker using two types of features and test it on two benchmark datasets. Experimental results demonstrate the superiority of our proposed approach when compared with other state-of-the-art trackers. project homepage http://bmal.hust.edu.cn/project/KMF2JMTtracking.html
arXiv commentThis paper has been accepted by IEEE Transactions on Circuits and Systems for Video Technology. The MATLAB code of our method is available from our project homepage http://bmal.hust.edu.cn/project/KMF2JMTtracking.html
References in corpus (7)
- High-Speed Tracking with Kernelized Correlation Filters
- Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
- Learning Spatially Regularized Correlation Filters for Visual Tracking
- A Novel Performance Evaluation Methodology for Single-Target Trackers
- An Experimental Survey on Correlation Filter-based Tracking
- Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking
- Tracking Randomly Moving Objects on Edge Box Proposals