Global Identification of Tracklets in Video Using Long Range Identity Sensors

Reliable tracking of people in video and recovering their identities are of great importance to video analytics applications.For outdoor applications, long range identity sensors such as active RFID can provide good coverage in a large open space, though they only provide coarse location information. We propose a probabilistic approach using noisy inputs from multiple long range identity sensors to globally associate and identify fragmented tracklets generated by video tracking algorithms. We extend a network flow based data association model to recover tracklet identity efficiently. Our approach is evaluated using five minutes of video and active RFID measurements capturing four people wearing RFID tags and a couple of passersby. Simulation is then used to evaluate performance for larger number of targets under different scenarios.identities are of great importance to video analytics applications.For outdoor applications, long range identity sensors such as active RFID can provide good coverage in a large open space, though they only provide coarse location information. We propose a probabilistic approach using noisy inputs from multiple long range identity sensors to globally associate and identify fragmented tracklets generated by video tracking algorithms. We extend a network flow based data association model to recover tracklet identity efficiently. Our approach is evaluated using five minutes of video and active RFID measurements capturing four people wearing RFID tags and a couple of passersby. Simulation is then used to evaluate performance for larger number of targets under different scenarios.