Occlusion-Aware Multi-Object Tracking via Expected Probability of Detection
This addresses occlusion challenges in multi-object tracking for applications like surveillance or robotics, but it appears incremental as it builds on existing methods like the MBM filter.
The paper tackles the problem of multi-object tracking under occlusion by enhancing the standard point-object model to incorporate the probability of detection that accounts for all objects' presence, and it demonstrates this through a visual tracking application using the multi-Bernoulli mixture filter with marks.
This paper addresses multi-object systems, where objects may occlude one another relative to the sensor. The standard point-object model for detection-based sensors is enhanced so that the probability of detection considers the presence of all objects. A principled tracking method is derived, assigning each object an expected probability of detection, where the expectation is taken over the reduced Palm density, which means conditionally on the object's existence. The assigned probability thus considers the object's visibility relative to the sensor, under the presence of other objects. Unlike existing methods, the proposed method systematically accounts for uncertainties related to all objects in a clear and manageable way. The method is demonstrated through a visual tracking application using the multi-Bernoulli mixture (MBM) filter with marks.