Learning Affinities and Dependencies for Multi-Target Tracking using a CRF Model

We propose a learning-based Conditional Random Field (CRF) model for tracking multiple targets by progressively associating detection responses into long tracks. Tracking task is transformed into a data association problem, and most previous approaches developed heuristical parametric models or learning approaches for evaluating independent affinities between track fragments (tracklets). We argue that the independent assumption is not valid in many cases, and adopt a CRF model to consider both tracklet affinities and dependencies among them, which are represented by unary term costs and pairwise term costs respectively. Unlike pre-vious methods, we learn the best global associations instead of the best local affinities between tracklets, and transform the task of finding the best association into an energy min-imization problem. A RankBoost algorithm is proposed to select effective features for estimation of term costs in the CRF model, so that better associations have lower costs. Our approach is evaluated on challenging pedestrian data sets, and are compared with state-of-art methods. Exper-iments show effectiveness of our algorithm as well as im-provement in tracking performance. 1