Multi‐camera multiple vehicle tracking in urban intersections based on multilayer graphs. Issue 12 (26th October 2020)

• Record Type: Journal Article
• Authors: Delavarian, Mohadeseh; Reza Marouzi, Omid; Hassanpour, Hamid; Parizi, Reza M.; Khan, Mohammad S.
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 12(2020)
• Page Start: 1673
• Page End: 1690
• Publication Date: 2020-10-26
• Subjects: object tracking -- traffic engineering computing -- cameras -- graph theory -- road vehicles -- intelligent transportation systems -- image motion analysis -- image representation; multicamera multiple vehicle tracking -- urban intersections -- vehicle visual tracking -- intelligent transportation systems -- motion flows -- vehicle trajectories -- vehicle crossing -- layer neighbourhoods -- views information -- history -- mapped multilayer graphs -- min‐cost flow computing; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2020.0086
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : Vehicle visual tracking is a challenging issue in intelligent transportation systems. The tracking gets more challenging when vehicles change direction at intersections. Undetermined motion flows, occlusion, and congestion are the potential issues of vehicle tracking at intersections. In this study, a new method for tracking multiple vehicles from a multi‐view is proposed to overcome occlusion caused at the intersections with undetermined motion flows. In the authors' method, a multilayer graph is presented that assigns motion flows to distinct layers with different neighbourhoods for each layer represented by the graph's edges. Hence, the vehicle trajectories are distributed among layers such that vehicles entering from the same side with similar motion flows are assigned to the same layer. All multilayer graphs of different views are mapped to the graph of the selected view. Then, tracking is performed on the distinct layers of the mapped multilayer graph by computing min‐cost flows. In cases such as vehicle crossing, misdetection, or occlusion, the method can predict the vehicle's tracks by using history, layer neighbourhoods, and other views' information. Experimental results show a consistency of the ground truth and the analysis obtained using the proposed method in tracking vehicles in the inner part of the intersection.