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Human action recognition using multiple views: A comparative perspective on recent developments

Human action recognition using multiple views: A comparative...

2011 ACM Multimedia Conference, MM'11 and Co-Located Workshops - 2011 Joint ACM Workshop on Human Gesture and Behavior Understanding, J-HGBU'11

作者： Holte, Michael B. Moeslund, Thomas B. Tran, Cuong Trivedi, Mohan M. Department of Architecture Design and Media Technology Aalborg Universtity Denmark Niels Jernes Vej 14 9220 Aalborg Denmark Computer Vision and Robotic Research Laboratory University of California San Diego 9500 Gilman Drive San Diego CA 92093-0434 United States

ISBN: (纸本)9781450309981

This paper presents a review and comparative study of recent multi-view 2D and 3D approaches for human action recognition. The approaches are reviewed and categorized due to their nature. We report a comparison of the most promising methods using two publicly available datasets: the INRIA Xmas Motion Acquisition Sequences (IXMAS) and the i3DPost Multi-View Human Action and Interaction Dataset. Additionally, we discuss some of the shortcomings of multi-view camera setups and outline our thoughts on future directions of 3D human action recognition. Copyright 2011 ACM.

关键词： Cameras

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A Collaborative Control System for Telepresence Robots

A Collaborative Control System for Telepresence Robots

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IEEE/RSJ International Conference on Intelligent robotics and Systems

作者： Douglas G. Macharet Dinei A. Florencio Computer Vision and Robotic Laboratory (VeRLab) Computer Science Department Universidade Federal de Minas Gerais MG Brazil Microsoft Research One Microsoft Way Redmond WA USA

ISBN: (纸本)9781467317375

Interest in telepresence robots is at an all time high, and several companies are already commercializing early or basic versions. There seems to be a huge potential for their use in professional applications, where they can help address some of the challenges companies have found in integrating a geographically distributed work force. However, teleoperation of these robots is typically a difficult task. This difficulty can be attributed to limitations on the information provided to the operator and to communication delay and failures. This may compromise the safety of the people and of the robot during its navigation through the environment. Most commercial systems currently control this risk by reducing size and weight of their robots. research effort in addressing this problem is generally based on "assisted driving", which typically adds a "collision avoidance" layer, limiting or avoiding movements that would lead to a collision. In this article, we bring assisted driving to a new level, by introducing concepts from collaborative driving to telepresence robots. More specifically, we use the input from the operator as a general guidance to the target direction, then couple that with a variable degree of autonomy to the robot, depending on the task and the environment. Previous work has shown collision avoidance makes operation easier and reduce the number of collisions. In addition (and in contrast to traditional collision avoidance systems), our approach also reduces the time required to complete a circuit, making navigation easier, safer, and faster. The methodology was evaluated through a controlled user study (N=18). Results show that the use of the proposed collaborative control helped reduce the number of collisions (none in most cases) and also decreased the time to complete the designated task.

关键词： integrating proposed making

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Learning How to Increase the Chance of Human-Robot Engagement

Learning How to Increase the Chance of Human-Robot Engagemen...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Douglas G. Macharet Dinei A. Florencio Computer Vision and Robotic Laboratory (VeRLab) Computer Science Department Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Microsoft Research One Microsoft Way Redmond WA USA

ISBN: (纸本)9781467363563

The increasing use of mobile robots in social contexts makes it important to provide them with the ability to behave in the most socially acceptable way possible. In this paper we investigate the problem of making a robot learn how to approach a person in order to increase the chance of a successful engagement. We propose the use of Gaussian Process Regression (GPR), combined with ideas from reinforcement learning to make sure the space is properly and continuously explored. In the proposed example scenario, this is used by the robot to predict the best decisions in relation to its position in the environment and approach distance, each one accordingly to a certain time of the day. Numerical simulations show a significant performance improvement when compared with a random technique. The robot is able to improve performance after just one day of interaction (a few dozens of trials), and achieves the maximum expected value for the proposed approach within sixty days.

关键词： Chance of Human-Robot Engagement social contexts makes provide

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robotic AND ARTIFICIAL-INTELLIGENCE SYSTEMS FOR THE NAVAL OPERATIONAL ENVIRONMENT

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NAVAL ENGINEERS JOURNAL 1987年第4期99卷 74-86页

作者： HOGGE, SM The author earned her bachelor's of science degree in industrial engineering and operations research from Virginia Polytechnic Institute and State University. While at Virginia Tech she worked in the Robotics Laboratory supporting robotics research sponsored by the National Bureau of Standards. She completed her master's of science in computer science at The Johns Hopkins University. Until this past March Miss Hogge worked at the Naval Surface Weapons Center in Silver Spring Maryland. Her duties included the application of robotics and artificial intelligence to explosive ordnance disassembly mobile robotics remote controlled fire fighting vehicles human factors expert systems computer enhanced decision-making aids and hardware security. She published a two-volume study discussing shipboard operational applications of robotics and AI. In addition her publications include nine recent journal articles on robotics and computer security. Miss Hogge is currently employed with Automaker Inc. in Houston Texas. Her current role is manager of all government related robotic operations and vision systems. Miss Hogge's projects at Automaker include: an explosive ordnance disassembly gantry robot system a vision inspection system to examine bearings 3-part camouflage spray painting an investigation of the use of robotic welding in Navy shipyards and rework facilities and epoxy spray painting of missile radomes.

Due to demographical factors, there will be a 25% decline in the national labor pool of eligible 17 to 21 year old men by 1992. As the Navy faces fierce competition with other services and private industry for the dwindling personnel resources of the nation, efforts must be made to consider automating certain tasks, especially those that are hazardous, boring, and labor intensive onboard ships at sea. The Surface Ship Continuing Concept Formulation (CONFORM) Program (SEA-5014) sponsored the Naval Surface Weapons Center's robotics laboratory to identify potential applications of robotic and artificial intelligence systems to operation and mission activities for shipboard use. The results of the investigation concerning applications of robotics to automate the aforementioned tasks are presented. Changes in current manning levels for selected tasks and ship classes, i.e., CVs, CVNs, LPDs, LPNs, and auxiliaries are examined. Future ship designs incorporating robotics, such as the advance based repair (ABR) ship are discussed. Twenty-four applications were cited in the study which include a remote controlled vehicle for flight deck operations, limited tasks in biological, chemical, and radiation environments, computer aided command decision aids, ordnance handling, undersea search, recovery, and salvage, and ventilation duct cleaning. Four of the applications and their subsequent hardware fruition will be discussed.

关键词： roboticS

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