A Sparse Probabilistic Learning Algorithm for Real-Time Tracking

Abstract

We address the problem of applying powerful pattern recognition algorithms based on kernels to efficient visual tracking. Recently S. Avidan, (2001) has shown that object recognizers using kernel-SVMs can be elegantly adapted to localization by means of spatial perturbation of the SVM, using optic flow. Whereas Avidan's SVM applies to each frame of a video independently of other frames, the benefits of temporal fusion of data are well known. Using a fully probabilistic 'relevance vector machine' (RVM) to generate observations with Gaussian distributions that can be fused over time is addressed. To improve performance further, rather than adapting a recognizer, we build a localizer directly using the regression form of the RVM. A classification SVM is used in tandem, for object verification, and this provides the capability of automatic initialization and recovery. The approach is demonstrated in real-time face and vehicle tracking systems. The 'sparsity' of the RVMs means that only a fraction of CPU time is required to track at frame rate. Tracker output is demonstrated in a camera management task in which zoom and pan are controlled in response to speaker/vehicle position and orientation, over an extended period. The advantages of temporal fusion in this system are demonstrated.