Better Feature Tracking Through Subspace Constraints
Channel: Bryan Poling
Date: 30/05/2014

Video Summary of CVPR 2014 paper: Better Feature Tracking Through Subspace Constraints
Authors: Bryan Poling, Gilad Lerman, Arthur Szlam

Feature tracking in video is a crucial task in computer vision. Usually, the tracking problem is handled one feature at a time, using a single-feature tracker like the Kanade-Lucas-Tomasi algorithm, or one of its derivatives. While this approach works quite well when dealing with high-quality video and "strong" features, it often falters when faced with dark and noisy video containing low-quality features. We present a framework for jointly tracking a set of features, which enables sharing information between the different features in the scene. We show that our method can be employed to track features for both rigid and non-rigid motions (possibly of few moving bodies) even when some features are occluded. Furthermore, it can be used to significantly improve tracking results in poorly-lit scenes (where there is a mix of good and bad features). Our approach does not require direct modeling of the structure or the motion of the scene, and runs in real time on a single CPU core.

Paper: http://arxiv.org/pdf/1405.2316v1.pdf
Code: http://www.math.umn.edu/~lerman/RCTracking/