Occlusion-aware Visual Tracker using Spatial Structural Information and
Dominant Features
Rongtai
Cai1 and Peng Zhu2
1Fujian Provincial
Engineering Technology Research Center of Photoelectric Sensing Application,
College of Photonic and Electronic Engineering, Fujian Normal University, China
2Fujian Newland Computer Co Ltd.,
China
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.
Keywords: Visual tracking, feature fusion,
occlusion-aware tracking, particle filter, part-based tracking.
Received September 9, 2019; accepted October 5, 2020