high performance computing on graphics processing units: hgpu.org

CAMShift is a well-established and fundamental algorithm for kernel-based visual object tracking. While it performs well with objects that have a simple and constant appearance, it is not robust in more complex cases. As it solely relies on back projected probabilities it can fail in cases when the object’s appearance changes (e.g., due to object or camera movement, or due to lighting changes), when similarly colored objects have to be re-detected or when they cross their trajectories. We propose low-cost extensions to CAMShift that address and resolve all of these problems. They allow the accumulation of multiple histograms to model more complex object appearances and the continuous monitoring of object identities to handle ambiguous cases of partial or full occlusion. Most steps of our method are carried out on the GPU for achieving real-time tracking of multiple targets simultaneously. We explain efficient GPU implementations of histogram generation, probability back projection, computation of image moments, and histogram intersection. All of these techniques make full use of a GPU’s high parallelization capabilities.