Estimating pose of articulated objects using low-level motion |
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| Authors: | Ben Daubney David Gibson Neill Campbell |
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| Affiliation: | 1. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China;2. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA;1. School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA 5005, Australia;2. College of Engineering and Science, Victoria University, Melbourne, Vic. 8001, Australia;3. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, China;4. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;1. Information Technology Engineering Department, School of Engineering, Tarbiat Modares University, Tehran, Iran;2. Visiting Lecturer; Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran;3. Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran |
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| Abstract: | In this work a method is presented to track and estimate pose of articulated objects using the motion of a sparse set of moving features. This is achieved by using a bottom-up generative approach based on the Pictorial Structures representation 1]. However, unlike previous approaches that rely on appearance, our method is entirely dependent on motion. Initial low-level part detection is based on how a region moves as opposed to its appearance. This work is best described as Pictorial Structures using motion. A standard feature tracker is used to automatically extract a sparse set of features. These features typically contain many tracking errors, however, the presented approach is able to overcome both this and their sparsity. The proposed method is applied to two problems: 2D pose estimation of articulated objects walking side onto the camera and 3D pose estimation of humans walking and jogging at arbitrary orientations to the camera. In each domain quantitative results are reported that improve on state of the art. The motivation of this work is to illustrate the information present in low-level motion that can be exploited for the task of pose estimation. |
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