We present an extension to the disjoint paths problem in which additional \emph{lifted} edges are introduced to provide path connectivity priors. We call the resulting optimization problem the lifted disjoint paths problem. We show that this problem is NP-hard by reduction from integer multicommodity flow and 3-SAT. To enable practical global optimization, we propose several classes of linear inequalities that produce a high-quality LP-relaxation. Additionally, we propose efficient cutting plane algorithms for separating the proposed linear inequalities. The lifted disjoint path problem is a natural model for multiple object tracking and allows an elegant mathematical formulation for long range temporal interactions. Lifted edges help to prevent id switches and to re-identify persons. Our lifted disjoint paths tracker achieves nearly optimal assignments with respect to input detections. As a consequence, it leads on all three main benchmarks of the MOT challenge, improving significantly over state-of-the-art.
Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland Informatics Campus（萨尔兰信息学园区）；Institute for Information Processing, Leibniz University Hannover（莱布尼茨汉诺威大学信息处理研究所）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Unsupervised Multiple Person Tracking using AutoEncoder-Based Lifted Multicuts
Fraunhofer Center Machine Learning（弗劳恩霍夫机器学习中心）；Fraunhofer ITWM（弗劳恩霍夫工业数学研究所）；Institute for Machine Learning and Analytics (IMLA) Offenburg University（奥芬堡大学机器学习与分析研究所（IMLA））；University of Mannheim（曼海姆大学）
LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；Leibniz University Hannover（汉诺威莱布尼茨大学）；Oldenburg University（奥尔登堡大学）
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Key Laboratory of Network System and Network Culture（网络系统与网络文化北京市重点实验室）；Key Laboratory of Interactive Technology and Experience System Ministry of Culture and Tourism（交互技术与体验系统文化和旅游部重点实验室）
VastMAT: A Large-Scale Multi-Category Benchmark for Multi-Animal Tracking
Dalian University of Technology（大连理工大学）；Wuhan University（武汉大学）；University of North Texas（北德克萨斯大学）；The Hong Kong University of Science and Technology（香港科技大学）
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA（多模态人工智能系统国家重点实验室（MAIS），中国科学院自动化所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；AutoLab, School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院AutoLab）；Anyverse Intelligence；Beijing Key Laboratory of Super Intelligent Security of Multi-Modal Information（北京超智能多模态信息安全重点实验室）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
