Adaptive Distraction Context Aware Tracking Based on Correlation Filter
Fei Feng, Xiao-Jun Wu, Tianyang Xu, Josef Kittler, Xue-Feng Zhu

TL;DR
This paper introduces an adaptive correlation filter-based tracking method that uses distractive objects as negative samples to enhance tracking accuracy amid occlusion and rotation.
Contribution
It proposes a novel adaptive method that identifies and utilizes distractive objects as negative samples within correlation filter tracking.
Findings
Improved tracking accuracy in challenging scenarios
Effective handling of occlusion and rotation
Adaptive use of distractive objects enhances robustness
Abstract
The Discriminative Correlation Filter (CF) uses a circulant convolution operation to provide several training samples for the design of a classifier that can distinguish the target from the background. The filter design may be interfered by objects close to the target during the tracking process, resulting in tracking failure. This paper proposes an adaptive distraction context aware tracking algorithm to solve this problem. In the response map obtained for the previous frame by the CF algorithm, we adaptively find the image blocks that are similar to the target and use them as negative samples. This diminishes the influence of similar image blocks on the classifier in the tracking process and its accuracy is improved. The tracking results on video sequences show that the algorithm can cope with rapid changes such as occlusion and rotation, and can adaptively use the distractive objects…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Surveillance and Tracking Methods · Fire Detection and Safety Systems · Human Pose and Action Recognition
MethodsConvolution
