Long-term time-sensitive costs for CRF-based tracking by detection

Abstract

We present a Conditional Random Field (CRF) approach to tracking-by-detection in which we model pairwise factors linking pairs of detections and their hidden labels, as well as higher order potentials defined in terms of label costs. Our method considers long-term connectivity between pairs of detections and models cue similarities as well as dissimilarities between them using time-interval sensitive models. In addition to position, color, and visual motion cues, we investigate in this paper the use of SURF cue as structure representations. We take advantage of the MOTChallenge 2016 to refine our tracking models, evaluate our system, and study the impact of different parameters of our tracking system on performance.