Globally optimal target tracking in real time using max-flow network

We propose a general framework for multiple target tracking across multiple cameras using max-flow networks. The framework integrates target detection, tracking, and classification from each camera and obtains the cross-camera trajectory of each target. The global data association problem is formed as a maximum a posteriori (MAP) problem and represented by a flow network. Similarities of time, location, size, and appearance (classification and color histogram) of the target across cameras are provided as inputs to the network and the target's optimal cross-camera trajectory is found using the max-flow algorithm. The implemented system is designed for real-time process with high-resolution videos (10MB per frame). The framework is validated on high resolution camera networks with both overlapping and non-overlapping fields of view in urban scenes.