Adaptive Distraction Context Aware Tracking Based on Correlation Filter

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 around the target as negative samples to improve the accuracy of target tracking.