Better Feature Tracking Through Subspace Constraints
This addresses the challenge of robust feature tracking in low-quality video for computer vision applications, representing an incremental improvement over single-feature trackers.
The paper tackles the problem of feature tracking in dark and noisy video by introducing a framework for jointly tracking a set of features to share information, resulting in improved tracking for rigid and nonrigid motions and in poorly-lit scenes, with real-time performance on a single CPU core.
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 nonrigid 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.