Occlusion-aware Visual Tracker using Spatial Structural Information and Dominant Features
This addresses occlusion issues in visual tracking, which is a domain-specific problem for computer vision applications, and appears to be an incremental improvement over existing methods.
The paper tackled the problem of occlusion in visual tracking by proposing an algorithm that divides objects into patches, extracts dominant features, and uses spatial structure within a particle filter framework, resulting in a tracker that outperforms comparison algorithms on color image sequences.
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.