The Solution Path Algorithm for Identity-Aware Multi-object Tracking

We propose an identity-aware multi-object tracker based on the solution path algorithm. Our tracker not only produces identity-coherent trajectories based on cues such as face recognition, but also has the ability to pinpoint potential tracking errors. The tracker is formulated as a quadratic optimization problem with l0 norm constraints, which we propose to solve with the solution path algorithm. The algorithm successively solves the same optimization problem but under different lp norm constraints, where p gradually decreases from 1 to 0. Inspired by the success of the solution path algorithm in various machine learning tasks, this strategy is expected to converge to a better local minimum than directly minimizing the hardly solvable l0 norm or the roughly approximated l1 norm constraints. Furthermore, the acquired solution path complies with the 'decision making process' of the tracker, which provides more insight to locating potential tracking errors. Experiments show that not only is our proposed tracker effective, but also the solution path enables automatic pinpointing of potential tracking failures, which can be readily utilized in an active learning framework to improve identity-aware multi-object tracking.