Max-Margin Offline Pedestrian Tracking with Multiple Cues

Abstract

In this paper, we introduce MMTrack, a hybrid single pedestrian tracking algorithm that puts together the advantages of descriptive and discriminative approaches for tracking. Specifically, we combine the idea of cluster-based appearance modeling and online tracking and employ a max-margin criterion for jointly learning the relative importance of different cues to the system. We believe that the proposed framework for tracking can be of general interest since one can add or remove components or even use other trackers as features in it which can lead to more robustness against occlusion, drift and appearance change. Finally, we demonstrate the effectiveness of our method quantitatively on a real-world data set.