標題: Multiple target tracking in occlusion area with interacting object models in urban environments
作者: Chen, Jiun-Fu
Wang, Chieh-Chih
Chou, Cheng-Fu
資訊工程學系
Department of Computer Science
關鍵字: Multitarget tracking;Interaction;LIDAR
公開日期: 1-May-2018
摘要: Multiple target tracking in crowded urban environments is a daunting task. High crowdedness complicates motion modeling, and occlusion makes tracking difficult as well. Based on the variable-structure multiple -model (VSMM) estimation framework, this paper extends an interacting object tracking (IOT) scheme with occlusion detection and a virtual measurement model for occluded areas. IOT is composed of a scene interaction model and a neighboring object interaction model. The scene interaction model considers the long-term interactions of a moving object and surroundings, and the neighboring object interaction model considers three short-term interactions. With these interacting object models, the motion feature of a moving object can be represented with the weight of each model. A virtual measurement model is proposed to exploit the motion feature with the IOT scheme under occlusion. The proposed approach was validated using a stationary 2D LIDAR. To verify in occlusion, a 3D LIDAR based benchmark system was developed to extract occluded moving segments. The ample experimental results show that the proposed IOT scheme tracks over 57% of occluded moving objects in an urban intersection. (C) 2018 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.robot.2018.02.004
http://hdl.handle.net/11536/144904
ISSN: 0921-8890
DOI: 10.1016/j.robot.2018.02.004
期刊: ROBOTICS AND AUTONOMOUS SYSTEMS
Volume: 103
起始頁: 68
結束頁: 82
Appears in Collections:Articles