A context-based tracker switching framework

We present a robust framework for tracking people in crowded outdoor environments monitored by multiple cameras with a goal of real-time performance. Since no single algorithm is perfect for the task of object tracking in all cases, we instead take an alternate approach. Our algorithm dynamically switches between several available trackers on-the-fly by evaluating the current state/context of the scene. Autonomous agents that make the switching decisions are assigned to each object in the scene. Initialization of new agents and the handoff between various tracking algorithms are completely automated. The collaboration between different trackers is shown to improve performance compared to the individual methods in terms of both computation and reliability. The tracker switching framework is evaluated on a multi-camera dataset and both qualitative and quantitative results are presented.