Learning to associate: HybridBoosted multi-target tracker for crowded scene

We propose a learning-based hierarchical approach of multi-target tracking from a single camera by progressively associating detection responses into longer and longer track fragments (tracklets) and finally the desired target trajectories. To define tracklet affinity for association, most previous work relies on heuristically selected parametric models; while our approach is able to automatically select among various features and corresponding non-parametric models, and combine them to maximize the discriminative power on training data by virtue of a HybridBoost algorithm. A hybrid loss function is used in this algorithm because the association of tracklet is formulated as a joint problem of ranking and…