Automatic detection and tracking of multiple interacting targets from a moving platform

In real-world scenarios, a target tracking system could be severely compromised by interactions, i.e., influences from the proximity and/or behavior of other targets or background objects. Closely spaced targets are difficult to distinguish, and targets may be partially or totally invisible for uncontrolled durations when occluded by other objects. These situations are very likely to degrade the performance or cause the tracker to fail because the system may use invalid target observations to update the tracks. To address these issues, we propose an integrated multitarget tracking system. A background-subtraction- based method is used to automatically detect moving objects in video frames captured by a moving camera. The data association method evaluates the overlap rates between newly detected objects (observations) and already-tracked targets and makes decisions pertaining to whether a target is interacting with other targets and whether it has a valid observation. According to the association results, distinct strategies are employed to update and manage the tracks of interacting versus wellisolated targets. This system has been tested with real-world airborne videos from the DARPA Video Verification of Identity program database and demonstrated excellent track continuity in the presence of occlusions and multiple target interactions, very low false alarm rate, and real-time operation on an ordinary general-purpose computer.