Global optimal data association for multiple people tracking

Abstract

Multiple people tracking is an important component for different tasks such as video surveillance and human-robot interaction. In this paper, a global optimization approach is proposed for long-term tracking of an a priori unknown number of targets, particularly aim to improve the robustness in case of complex interaction and mutual occlusion. With a state-space discretization scheme, the multiple object tracking problem is formulated with a grid-based network flow model, resulting in a convex problem that can be casted into an Integer Linear Programming (ILP), then solved through relaxation. In order to allow recovery from misdetections, common heuristics such as non-maxima suppression is eschewed within observations. In addition, we show that how behavior cue can be integrated into the association affinity model, providing discriminative hints for resolving ambiguities between crossing trajectories. The validity of the proposed method is demonstrated through experiments on multiple challenging video sequences, using a calibrated multi-camera setup.