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

Authors: Chanuk Lim; Jeonghwan Gwak; Moongu Jeon

Addresses: Smart Power Distribution Laboratory, Korea Electric Power Research Institute (KEPRI), Daejeon 34056, South Korea ' Biomedical Research Institute and Department of Radiology, Seoul National University Hospital (SNUH), Seoul 03080, South Korea ' School of Information and Communications, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, South Korea

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.

Keywords: multi-object tracking; data association; min-cost flow network.

DOI: 10.1504/IJCVR.2018.095588

International Journal of Computational Vision and Robotics, 2018 Vol.8 No.6, pp.572 - 590

Accepted: 05 Mar 2018
Published online: 11 Oct 2018 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article