Tracking multiple objects is a challenging task when objects move in groups and occlude each other. Existing methods have investigated the problems of group division and group energy-minimization; however, lacking overall object-group topology modeling limits their ability in handling complex object and group dynamics. Inspired with the social affinity property of moving objects, we propose a Graphical Social Topology (GST) model, which estimates the group dynamics by jointly modeling the group structure and the states of objects using a topological representation. With such topology representation, moving objects are not only assigned to groups, but also dynamically connected with each other, which enables in-group individuals to be correctly associated and the cohesion of each group to be precisely modeled. Using well-designed topology learning modules and topology training, we infer the birth/death and merging/splitting of dynamic groups. With the GST model, the proposed multi-object tracker can naturally facilitate the occlusion problem by treating the occluded object and other in-group members as a whole while leveraging overall state transition. Experiments on both RGB and RGB-D datasets confirm that the proposed multi-object tracker improves the state-of-the-arts especially in crowded scenes.
Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing
Industrial Systems Institute, Athena Research Center, Patras Science Park, Greece（工业系统研究所，阿提卡研究中心，帕特拉斯科学公园，希腊）；AviSense.AI, Patras Science Park, Greece（AviSense.AI，帕特拉斯科学公园，希腊）；Dpt. of Informatics & Telecom., University of Ioannina, Arta, Greece（信息与电信系，伊奥安妮亚大学，阿塔，希腊）
Joint Counting, Detection and Re-Identification for Multi-Object Tracking
Shenzhen Duorou Intelligent System Co., Ltd.（深圳多柔智能系统有限公司）；School of Electrical Engineering, Xi’an Jiaotong University（西安交通大学电气工程学院）；State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science（中国科学院沈阳自动化研究所机器人国家重点实验室）
A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects
University of Freiburg（弗莱堡大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
最近，最小成本多分割公式已被提出，并被证明是成功的运动轨迹分割和多目标跟踪的情况下。这两个任务都受益于将图形模型分解为基于吸引和排斥成对项的最佳数量的连接组件。这两个任务在不同的粒度级别上制定，因此，主要利用局部信息进行运动分割，主要利用高级信息进行多目标跟踪。在本文中，我们认为，点轨迹和它们的局部关系可以有助于多目标跟踪的高层次任务，也认为，高层次的线索，从目标检测和跟踪有助于解决运动分割。我们提出了一个联合图形模型的点轨迹和目标检测的多割运动分割{ \it和}多目标跟踪问题的解决方案在一次。FBMS 59运动分割基准以及2D MOT 2015基准的行人跟踪序列的结果证明了这种联合方法的前景。
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医学机器人研究所）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）；Shanghai AI Lab（上海人工智能实验室）
