LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking

• URL: http://arxiv.org/abs/2111.11892v1
• Date: Tue, 23 Nov 2021 14:09:47 GMT
• Title: LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking
• Authors: Duy M. H. Nguyen, Roberto Henschel, Bodo Rosenhahn, Daniel Sonntag, Paul Swoboda
• Abstract summary: Multi-Camera Multi-Object Tracking is currently drawing attention in the computer vision field due to its superior performance in real-world applications. We propose a mathematically elegant multi-camera multiple object tracking approach based on a spatial-temporal lifted multicut formulation.
• Score: 42.87953709286856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-Camera Multi-Object Tracking is currently drawing attention in the computer vision field due to its superior performance in real-world applications such as video surveillance with crowded scenes or in vast space. In this work, we propose a mathematically elegant multi-camera multiple object tracking approach based on a spatial-temporal lifted multicut formulation. Our model utilizes state-of-the-art tracklets produced by single-camera trackers as proposals. As these tracklets may contain ID-Switch errors, we refine them through a novel pre-clustering obtained from 3D geometry projections. As a result, we derive a better tracking graph without ID switches and more precise affinity costs for the data association phase. Tracklets are then matched to multi-camera trajectories by solving a global lifted multicut formulation that incorporates short and long-range temporal interactions on tracklets located in the same camera as well as inter-camera ones. Experimental results on the WildTrack dataset yield near-perfect result, outperforming state-of-the-art trackers on Campus while being on par on the PETS-09 dataset. We will make our implementations available upon acceptance of the paper.

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