Multi-target tracking plays a key role in many computer vision applications including robotics, human-computer interaction, event recognition, etc., and has received increasing attention in past several years. Starting with an object detector is one of many approaches used by existing multi-target tracking methods to create initial short tracks called tracklets. These tracklets are then gradually grouped into longer final tracks in a heirarchical framework. Although object detectors have greatly improved in recent years, these detectors are far from perfect and can fail to detect the object of interest or identify a false positive as the desired object. Due to the presence of false positives or mis-detections from the object detector, these tracking methods can suffer from track fragmentations and identity switches. To address this problem, we formulate multi-target tracking as a min-cost flow graph problem which we call the average shortest path. This average shortest path is designed to be less biased towards the track length. In our average shortest path framework, object misdetection is treated as an occlusion and is represented by the edges between track-let nodes across non consecutive frames. We evaluate our method on the publicly available ETH dataset. Camera motion and long occlusions in a busy street scene make ETH a challenging dataset. We achieve competitive results with lower identity switches on this dataset as compared to the state of the art methods.
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