Configuration of a min-cost flow network for data association in multi-object tracking

Abstract

In this paper, we mainly describe how to formulate a network flows optimally for multi-object tracking. The network flows can be used to construct trajectories of objects (between frames) to achieve multi-object tracking. The most important issue to establish such network is to design nodes and edges in the network. In this work, we propose a method to fuse the object detector with object trackers in order to efficiently design the nodes and edges. The object trackers can give the information on robust classifiers or features of objects through training, which helps to design the edges. This approach is significant when a detector fails due to occluded objects. If an object failed to be detected, the object tracker will be substituted to the object detector. In this way, we employ the object tracker and the object detector to formulate a sophisticated network depending on the condition. The proposed approach enables to eliminate the clutters and thus overcome the heavy occlusion situations. We evaluated performance of the proposed method through several experiments using real-world video sequences. The experimental results demonstrated good performance of the proposed approach compared to state-of-the-art methods.