Publication
LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking
Ho Minh Duy Nguyen; Roberto Henschel; Bodo Rosenhahn; Daniel Sonntag; Paul Swoboda
In: Conference on Computer Vision and Pattern Recognition (CVPR) 2022. International Conference on Computer Vision and Pattern Recognition (CVPR), June 21-24, IEEE/CVF, 2022.
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.
Projects
- pAItient - Protected Artificial Intelligence Innovation Environment for Patient Oriented Digital Health Solutions for developing, testing and evidence based evaluation of clinical value.
- XAINES - Explaining AI with Narratives