  In this work, we consider data association problems involving multi-object
tracking (MOT). In particular, we address the challenges arising from object
occlusions. We propose a framework called approximate dynamic programming track
(ADPTrack), which applies dynamic programming principles to improve an existing
method called the base heuristic. Given a set of tracks and the next target
frame, the base heuristic extends the tracks by matching them to the objects of
this target frame directly. In contrast, ADPTrack first processes a few
subsequent frames and applies the base heuristic starting from the next target
frame to obtain tentative tracks. It then leverages the tentative tracks to
match the objects of the target frame. This tends to reduce the occlusion-based
errors and leads to an improvement over the base heuristic. When tested on the
MOT17 video dataset, the proposed method demonstrates a 0.7% improvement in the
association accuracy (IDF1 metric) over a state-of-the-art method that is used
as the base heuristic. It also obtains improvements with respect to all the
other standard metrics. Empirically, we found that the improvements are
particularly pronounced in scenarios where the video data is obtained by
fixed-position cameras.
