Patch Flow based Visual Object Tracking
Nikita Prabhu, S. Avinash Ramakanth, R. Venkatesh Babu · 2014
This paper proposes a novel algorithm for object tracking using Approximate Nearest Neighbour Fields (ANNF). ANNF maps have been previously used to address several problems like denoising, image completion, re-targeting and medical image analysis. In this paper, we deal with the challenging problem of visual object tracking, using patch flow. The proposed method uses FeatureMatch to find patch correspondence between successive frames, enabling the tracker to find the best match for the object in the next frame. Based on the flow, each patch is labeled as either foreground or background. The proportion of FG/BG/border patches contributing to each pixel determines its final label. We show that objects can be successfully tracked across videos, under challenging conditions such as scale variations, illumination changes and occlusion using the proposed technique.