Online multi-object tracking combining optical flow and compressive tracking in Markov decision process

Abstract

Effective features are important for visual tracking, and efficiency also needs to be considered especially for multi-object tracking. Thanks to the simplicity, we think compressive sensing features are suitable for this task. In this paper, we use compressive sensing features to improve the Markov decision process (MDP) multi-object tracking framework. First, we design a single object tracker which uses the compressive tracking to correct the optical flow tracking and apply this tracker into the MDP tracking framework. The appearance model constructed during compressive tracking also helps for data association. In order to validate our method, we firstly test the designed single object tracker with a common dataset. Then, we test our multi-object tracking method for vehicle tracking. Finally, we analyze and test our approach in the multi-object tracking (MOT) benchmark for pedestrian tracking. The results show our approach performs superiorly against several state-of-the-art online multi-object trackers.