Probabilistic detection and tracking at high frame rates using affine warping

This paper addresses two vital issues that can affect real-time operation of a visual tracking system: the realization of an effective sub-sampling policy and the real-time initialization of the tracking algorithm. We propose to use affine warping to sub-sample the images selectively only in those regions that contain too much data for real-time operation. The automatic detection of objects of interest in images captured by a moving camera is based on a random search which enables us to set all thresholds automatically without any user support. Using these methods, we implemented a probabilistic tracker that can detect and track up to 10 objects at 60 Hz on a dual processor 933 MHz Pentium III PC.