Observe-and-explain: A new approach for multiple hypotheses tracking of humans and objects

This paper presents a novel approach for tracking humans and objects under severe occlusion. We introduce a new paradigm for multiple hypotheses tracking, observe-and-explain, as opposed to the previous paradigm of hypothesize-and- test. Our approach efficiently enumerates multiple possibilities of tracking by generating several likely 'explanations' after concatenating a sufficient amount of observations. The computational advantages of our approach over the previous paradigm under severe occlusions are presented. The tracking system is implemented and tested using the i-Lids dataset, which consists of videos of humans and objects moving in a London subway station. The experimental results show that our new approach is able to track humans and objects accurately and reliably even when they are completely occluded, illustrating its advantage over previous approaches.