LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking
This work addresses tracking in crowded or wide spaces for applications like video surveillance, representing an incremental improvement over existing methods.
The authors tackled multi-camera multi-object tracking by proposing a method that refines tracklets using 3D geometry projections to eliminate ID-switch errors and then solves a global lifted multicut formulation for data association. The approach achieved near-perfect performance on the WildTrack dataset, outperforming state-of-the-art on Campus and matching performance on PETS-09.
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 in crowded scenes or in wide spaces. 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 performance, outperforming state-of-the-art trackers on Campus while being on par on the PETS-09 dataset.