Robust Multi-Object Tracking via Cross-Domain Contextual Information for Sports Video Analysis
Published in ICASSP 2012.
Video (image-to-image) registration is a fundamental problem in computer vision. Registering video frames to the same coordinate system is necessary before meaningful inference can be made from a dynamic scene in the presence of camera motion. Standard registration techniques detect specific structures (e.g. points and lines), find potential correspondences, and use a random sampling method to choose inlier correspondences. Unlike these standards, we propose a parameter-free, robust registration method that avoids explicit structure matching by matching entire images or image patches. We frame the registration problem in a sparse representation setting, where outlier pixels are assumed to be sparse in an image. Here, robust video registration (RVR) becomes equivalent to solving a sequence of L1 minimization problems, each of which can be solved using the Inexact Augmented Lagrangian Method (IALM). Our RVR method is made efficient (sublinear complexity in the number of pixels) by exploiting a hybrid coarse-to-fine and random sampling strategy along with the temporal smoothness of camera motion. We showcase RVR in the domain of sports videos, specifically American football. Our experiments on real-world data show that RVR outperforms standard methods and is useful in several applications (e.g. automatic panoramic stitching and non-static background subtraction).
This study is supported by the research grant for the Human Sixth Sense Programme at the Advanced Digital Sciences Center from Singapore’s Agency for Science, Technology and Research (A*STAR).