Modeling and fusing negative information for dynamic extended multi-object tracking

A novel approach to utilizing negative information to improve the accuracy of extended multi-object tracking is presented. The parameterized probability density of object tracks unresolved in sensor data is updated via inferences about the sensor-to-object geometries necessary to result in occlusion of the unresolved object. Negative information is also leveraged to improve data association and to enable a novel death model, all of which contribute to a more accurate and precise belief of the local scene. Simulation and experimental results are presented from a common autonomous driving scenario.