Occlusion-aware Visual Tracker using Spatial Structural Information and Dominant Features

TL;DR

This paper introduces an occlusion-aware visual tracking algorithm that segments objects into patches, extracts dominant features, and uses spatial structure cues within a particle filter framework to improve robustness against occlusion.

Contribution

It presents a novel occlusion-aware tracking method combining patch-based segmentation, dominant feature extraction, and spatial structure cues within a particle filter.

Findings

Outperforms comparison algorithms in occlusion handling

Demonstrates robustness across different image resolutions

Enhances tracking accuracy during occlusion events

Abstract

To overcome the problem of occlusion in visual tracking, this paper proposes an occlusion-aware tracking algorithm. The proposed algorithm divides the object into discrete image patches according to the pixel distribution of the object by means of clustering. To avoid the drifting of the tracker to false targets, the proposed algorithm extracts the dominant features, such as color histogram or histogram of oriented gradient orientation, from these image patches, and uses them as cues for tracking. To enhance the robustness of the tracker, the proposed algorithm employs an implicit spatial structure between these patches as another cue for tracking; Afterwards, the proposed algorithm incorporates these components into the particle filter framework, which results in a robust and precise tracker. Experimental results on color image sequences with different resolutions show that the proposed tracker outperforms the comparison algorithms on handling occlusion in visual tracking.