Dynamic Subset Selection for Multi-Camera Tracking

While multi-camera methods for object tracking tend to outperform their single-camera counterparts, the data aggregation schemes can introduce new challenges, such as resource management and algorithm complexity. We present a framework for dynamically choosing the best subset of available cameras for tracking in real-time, which reduces aggregate tracking error and resource consumption and can be applied to a variety of existing base tracking models. We demonstrate on challenging video sequences of players in a basketball game. Our method is able to successfully track targets entering and exiting camera views and through occlusions, and overcome instances of single-view tracking drift.