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检索条件"机构=The Computer Vision And Robotics Research Laboratory"

共 234 条记录，以下是71-80 订阅

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Introducing “XMOB”: Extremity Movement Observation Framework for Upper Body Pose Tracking in 3D

Introducing “XMOB”: Extremity Movement Observation Framewo...

引用

IEEE International Symposium on Multimedia

作者： Cuong Tran Mohan M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego CA USA

We introduce a vision based, marker less upper body pose tracking approach that first tracks the 3D movements of extremities, including head and hands. Then based on the knowledge of upper body model, these extremity movements are used to predict the whole upper body motion as an inverse kinematics problem. The experimental validation showed the promise of applying this approach in several smart environments and HCI situations, e.g. user activity observation in driving scene, meeting room, teleconference scene.

关键词： Extremities Tracking Head Humans Kinematics Predictive models Layout Laboratories Teleconferencing Motion estimation

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Head and gaze dynamics in visual attention and context learning

Head and gaze dynamics in visual attention and context learn...

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IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Anup Doshi Mohan M. Trivedi Laboratory for Intelligent and Safe Automobiles Computer Vision and Robotics Research Laboratory University of California San Diego USA

Future intelligent environments and systems may need to interact with humans while simultaneously analyzing events and critical situations. Assistive living, advanced driver assistance systems, and intelligent command-and-control centers are just a few of these cases where human interactions play a critical role in situation analysis. In particular, the behavior or body language of the human subject may be a strong indicator of the context of the situation. In this paper we demonstrate how the interaction of a human observer's head pose and eye gaze behaviors can provide significant insight into the context of the event. Such semantic data derived from human behaviors can be used to help interpret and recognize an ongoing event. We present examples from driving and intelligent meeting rooms to support these conclusions, and demonstrate how to use these techniques to improve contextual learning.

关键词： Humans Intelligent systems Intelligent robots Layout Monitoring Air traffic control Vehicle dynamics Cameras Laboratories Intelligent vehicles

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Unsupervised learning of motion patterns of rear surrounding vehicles

Unsupervised learning of motion patterns of rear surrounding...

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IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： Brendan Morris Mohan Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA USA

Awareness to a vehicle's surrounding is necessary for safe driving. Current surround technologies focus on the detection of obstacles in hard-to-view places but may neglect temporal information. This paper seeks the causes of dangerous situations by examining surround behavior. A general hierarchical learning framework is introduced to automatically learn surround behaviors. By observing motion trajectories during natural driving, models of rear vehicle behaviors are obtained in an unsupervised fashion. The extracted behaviors are shown to correspond to typical driving scenarios, vehicle overtake and surround overtake, demonstrating the effectiveness of the learning framework.

关键词： Unsupervised learning Vehicles

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A framework for automating the construction of computational models

A framework for automating the construction of computational...

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Congress on Evolutionary Computation

作者： E. Hourdakis P. Trahanias Department of Computer Science University of Crete Greece Computational Vision and Robotics Laboratory Foundation for Research and Technology - Hellas Heraklion Greece

Computational modeling of natural systems can be used for interdisciplinary applications, such as the configuration of robotic systems or the validation of biological ones. Up to date there has been a little progress on suggesting a framework for automating the process of creating a computational model for biological processes. Instead researchers focus on the implementations of systems that are intended to replicate a tight set of biological behaviors. Such framework should be able to construct any system based on the appropriate level of abstraction chosen by the designer, as well as be able to enforce the appropriate biological consistency without compromising on performance or scalability of the generated models. In this paper we propose a framework that can automate the construction of computational models using genetic algorithms and demonstrate how this framework can construct a model of the parieto-frontal and premotor regions involved in grasping.

关键词： Computational modeling Biological system modeling Biology computing Biological systems computer science Brain modeling Genetic algorithms computer vision Robot vision systems Laboratories

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Time perception in shaping cognitive neurodynamics of artificial agents

Time perception in shaping cognitive neurodynamics of artifi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Michail Maniadakis Jun Tani Panos Trahanias Computational Vision and Robotics Laboratory Institute of Computer Science Foundation for Research and Technology Hellas Crete Greece Laboratory for Behavior and Dynamic Cognition RIKEN Brain Science Institute Wako Saitama Japan

Recent research in cognitive systems aims to uncover important aspects of biological cognitive processes and additionally formulate design principles for implementing artificially intelligent systems. Despite the increasing amount of research efforts addressing cognitive phenomena, the issue of time perception and how it is linked to other cognitive processes remains largely unexplored. In the current paper, we make a first attempt for studying artificial time perception by means of simulated robotic experiments. Specifically, we investigate a behavioral rule switching task consisting of repeating trials with dynamic temporal duration. An evolutionary process is used to search for neuronal mechanisms accomplishing the rule switching task taking also into account its particular temporal characteristics. Our repeated simulation experiments showed that (i) time perception and ordinary cognitive processes may co-exist in the system sharing the same neural resources, and (ii) time perception dynamics bias the functionality of neural mechanisms with other cognitive responsibilities. Finally, in the current paper we make contact of the obtained results with previous brain imaging studies on time perception, and we make predictions for possible time-related dynamics in the real brain.

关键词： Neurodynamics Cognitive robotics Intelligent robots Intelligent systems Computational and artificial intelligence Robot vision systems Laboratories Brain modeling Humans computer vision

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An adaptive scene description for activity analysis in surveillance video

An adaptive scene description for activity analysis in surve...

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作者： Morris, Brendan Trivedi, Mohan Computer Vision and Robotics Research Laboratory University of California San Diego San Diego CA 92093-0434 United States

ISBN: (纸本)9781424421756

This paper presents an adaptive framework for live video analysis. The activities of surveillance subjects are described using a spatio-temporal vocabulary learned from recurrent motion patterns. The repetitive nature of object trajectories are used to build a topographical map, where nodes are points of interest and the edges correspond to activities, to describe a scene. The graph is learned in an unsupervised manner but is flexible and able to adjust to changes in the environment or other scene variations. © 2008 IEEE.

关键词： Security systems

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Learning and classification of trajectories in dynamic scenes: A general framework for live video analysis

Learning and classification of trajectories in dynamic scene...

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IEEE 5th International Conference on Advanced Video and Signal Based Surveillance, AVSS 2008

作者： Morris, Brendan T. Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA 92093-0434 United States

ISBN: (纸本)9780769533414

This paper presents a general framework for live video analysis. The activities of surveillance subjects are described using a spatio-temporal vocabulary learned from recurrent motion patterns. The repetitive nature of object trajectories is used to build a topographical scene description where nodes are points of interest (POI) and the edges correspond to activity paths (AP). The POI are learned through as a mixture of Gaussians and AP by clustering trajectories. The paths are probabilistically represented by hidden Markov models and adapt to temporal variations using online maximum likelihood regression (MLLR) and through a periodic batch update. Using the scene graph, new trajectories can be analyzed in online fashion to categorize past and present activity, predict future behavior, and detect abnormalities. ©2008 IEEE.

关键词： Trajectories

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Human body modelling and tracking using volumetric representation: Selected recent studies and possibilities for extensions

Human body modelling and tracking using volumetric represent...

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Cuong Tran Mohan M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

Articulated human body modeling and tracking from vision data is an attractive research area with many potential applications. There has been a tremendous amount of related research works in this area. Therefore, having a comprehensive insight into high quality existing works and awareness of the research frontier in the area is essential for follow-up research studies. With that objective, this paper provides a review of the subarea of model based methods for human body modeling and tracking using volumetric (voxel) data. We will focus on analyzing and comparing some recent techniques, especially which are in the past two years, in order to highlight trends in the domain as well as to point out limitations of the current state of the art. Based on this analysis, we will discuss our idea of combining Laplacian Eigenspace (LE) based voxel segmentation [20] and Kinematically Constrained Gaussian mixture model (KC-GMM) method [3] to have a more powerful human body pose estimation system as well as discuss other possibilities for future work.

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Particle filtering with rendered models: A two pass approach to multi-object 3D tracking with the GPU

Particle filtering with rendered models: A two pass approach...

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IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Erik Murphy-Chutorian Mohan M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

We describe a new approach to vision-based 3D object tracking, using appearance-based particle filters to follow 3D model reconstructions. This method is targeted towards modern graphics processors, which are optimized for 3D reconstruction and are capable of highly parallel computation. We discuss an OpenGL implementation of this approach, which uses two rendering passes to update the particle filter weights. In the first pass, the system renders the previous object state estimates to an off-screen framebuffer. In the second pass, the system uses a programmable vertex shader to compute the mean normalized cross-correlation between each sample and the subsequent video frame. The particle filters are updated using the correlation scores and provide a full 3D track of the objects. We provide examples for tracking human heads in both single and multi-camera scenarios.

关键词： Filtering Particle tracking Rendering (computer graphics) Particle filters Target tracking Graphics Optimization methods Concurrent computing State estimation Humans

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Learning and Classification of Trajectories in Dynamic Scenes: A General Framework for Live Video Analysis

Learning and Classification of Trajectories in Dynamic Scene...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Brendan T. Morris Mohan M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego CA USA

This paper presents a general framework for live video analysis. The activities of surveillance subjects are described using a spatio-temporal vocabulary learned from recurrent motion patterns. The repetitive nature of object trajectories is used to build a topographical scene description where nodes are points of interest (POT) and the edges correspond to activity paths (AP). The POI are learned through as a mixture of Gaussians and AP by clustering trajectories. The paths are probabilistically represented by hidden Markov models and adapt to temporal variations using online maximum likelihood regression (MLLR) and through a periodic batch update. Using the scene graph, new trajectories can be analyzed in online fashion to categorize past and present activity, predict future behavior, and detect abnormalities.

关键词： Layout Surveillance Video compression Videoconference Signal analysis computer vision Cameras Monitoring Robot vision systems Laboratories

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