Tracking and identifying burglar using collaborative sensor-camera networks

This work presents BurTrap, a networking system which integrates wireless modules (such as TelosB nodes) with networked surveillance cameras to automatically, accurately, timely track and identify burglar who stole the property. First, we design an energy-efficient wakeup scheduling protocol that guarantees a successful target tracking while reducing the communication energy consumption of the portable wireless module. Then, we identify burglar among all the objects appeared in the obtained video information by performing trajectory fitting between the estimated geometric trajectory and the estimated local visual trajectory. Through extensive experiments, we show that BurTrap can pinpoint the burglar with extremely high accuracy.