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International Conference on Pattern Recognition

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

ISBN: (纸本)9781424421749

We propose an integrated framework for automated hand model initialization and tracking using voxel data. Starting with an initial specific hand pose, the Laplacian Eigenspace (LE) based segmentation method is applied to segment hand voxel into different parts. This segmentation result is then used to extend the kinematically constrained gaussian mixture model (KC-GMM) method for articulated body pose inference with an automated hand model initialization. Our experiment with both synthesized hand voxel and real hand voxel captured from multi-perspective thermal cameras show that by combining the two methods, we have a more powerful system than using each one solitarily.

关键词： Power system modeling Cameras Torso Human computer interaction Image reconstruction Kinematics computer vision Robot vision systems robotics and automation Laboratories

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3D tracking and dynamic analysis of human head movements and attentional targets

3D tracking and dynamic analysis of human head movements and...

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Erik Murphy-Chutorian Mohan M. Trivedi Systems for Human Interactivity Visualization and Analysis Computer Vision and Robotics Research Laboratory University of California San Diego USA

We present a new system-level framework for the automatic detection and tracking of multiple persons' heads in intelligent meeting rooms. We implement this approach with a distributed array of cameras that detect the meeting participants and continuously estimate their head orientation and head movements in 6 degrees-of-freedom with fine precision. The initial position of each person is obtained with a set of face detectors coupled with a new iterative approach to resolve the 3D ambiguities from overlapping epipolar lines. The head pose is obtained from a hybrid head pose estimation and tracking scheme that combines support vector regressors with a new multi-view 3D model-based tracking system. The purpose of this system is to facilitate the automatic semantic analysis of group meetings. As an example application, we evaluate the ability of the system to estimate the person that receives the most visual attention in the form of head direction.

关键词： Head Magnetic heads Cameras Three dimensional displays Face Estimation Visualization

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IEEE Transactions on Haptics: Editorial

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IEEE Transactions on Haptics 2008年第1期1卷 2-8页

作者： Edward Colgate, J. Lederman, Susan Prattichizzo, Domenico Barbagli, Federico Basdogan, Cagatay Buss, Martin Ernst, Marc Frisoli, Antonio Gillespie, Brent Grant, Danny Harders, Matthias Hayward, Vincent Jones, Lynette Kajimoto, Hiroyuki Kappers, Astrid Kheddar, Abderrahmane Lin, Ming C. MacLean, Karon Miller, Brian Noma, Haruo Okamura, Allison M. O'Malley, Mareia Ryu, Jeha Tan, Hong Z. Yokokohji, Yasuyoshi Department of Psychology School of Computing Queen's University Canada IEEE Robotics and Automation Society Tübingen Germany Stanford University's Computer Science Department Tübingen Germany Institute of Automatic Control Engineering Technische Universität München Germany Independent Research Group Multisensory Perception and Action Max Planck Institute for Biological Cybernetics Tübingen Germany Department of Applied Mechanics SSSA Japan Research Division Virtual Reality and Telerobotic Systems PERCRO Laboratory Japan Department of Mechanical Engineering Japan Department of Robotics McGill University Canada Computer Vision Lab ETH Zurich Japan Virtual Reality in Medicine Group Japan IEEE CS/RAS Technical Committee on Haptics Japan Department of Electrical and Computer Engineering McGill University Canada Department of Mechanical Engineering MIT Japan University of Electro-Communications Japan Department of Physics and Astronomy Utrecht University Netherlands University of Evry France Chapel Hill United States Department of Computer Science University of British Columbia Canada ATR Knowledge Science Lab United Kingdom Department of Mechanical Engineering Denmark Johns Hopkins University United States Laboratory for Computational Sensing and Robotics United Kingdom Engineering Research Center for Computer-Integrated Surgical Systems and Technology United States Mechanical Engineering and Materials Science Department Rice University United States Korea Republic of Department of Electrical and Computer Engineering Mechanical Engineering and Psychological Sciences Purdue University United States Department of Mechanical Engineering and Science Graduate School of Engineering Kyoto University Japan

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Satellite imagery based adaptive background models and shadow suppression

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Signal, Image and Video Processing 2007年第2期1卷 119-132页

作者： Doshi, Anup Trivedi, Mohan Manubhai Computer Vision and Robotics Research Laboratory University of California at San Diego San Diego CA 92093-0434 9500 Gilman Drive United States

Accurate segmentation of foreground objects in video scenes is critical for assuring reliable performance of vision systems for object tracking and situational awareness in outdoor scenes. Most existing techniques for background modeling and shadow suppression require that a number of parameters be "hand-tuned" based on environmental conditions. This paper presents two contributions to overcome such limitations. First, we develop and demonstrate a satellite imagery based approach for selecting appropriate background and shadow models. It is shown that the illumination conditions (i.e. cloud cover) of a scene can be reliably inferred from visible satellite images in the local region of the camera. The second contribution presented in the paper is introduction and evaluation of a Hybrid Cone-Cylinder Codebook (HC3) model which combines an adaptive efficient background model with HSV-color space shadow suppression into a single coherent framework. The structure of the HC3 model allows for seamless fusion of the satellite data. We are thereby able to exploit the fact that, for example, shadows are more pronounced on sunny days than cloudy days, allowing for more sensitive detection. The paper presents a set of experiments using day long sequences of videos from an operational surveillance system testbed. Results of these experimental analyses quantitatively illustrate the benefits of using satellite imagery to inform and adaptively adjust background and shadow modeling. © Springer-Verlag London Limited 2007.

关键词： Cloud radiance Foreground-background segmentation Hybrid Cone-Cylinder Codebook (HC3) Shadow suppression Satellite imagery

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Head Pose Estimation for Driver Assistance Systems: A Robust Algorithm and Experimental Evaluation

Head Pose Estimation for Driver Assistance Systems: A Robust...

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International Conference on Intelligent Transportation

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

Recognizing driver awareness is an important prerequisite for the design of advanced automotive safety systems. Since visual attention is constrained to a driver's field of view, knowing where a driver is looking provides useful cues about his activity and awareness of the environment. This work presents an identity-and lighting-invariant system to estimate a driver's head pose. The system is fully autonomous and operates online in daytime and nighttime driving conditions, using a monocular video camera sensitive to visible and near-infrared light. We investigate the limitations of alternative systems when operated in a moving vehicle and compare our approach, which integrates Localized Gradient Orientation histograms with support vector machines for regression. We estimate the orientation of the driver's head in two degrees-of-freedom and evaluate the accuracy of our method in a vehicular testbed equipped with a cinematic motion capture system.

关键词： Robustness Magnetic heads Automotive engineering Safety Cameras Remotely operated vehicles Histograms Support vector machines Motion estimation System testing

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Video Based Surround Vehicle Detection, Classification and Logging from Moving Platforms: Issues and Approaches

Video Based Surround Vehicle Detection, Classification and L...

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IEEE Symposium on Intelligent Vehicle

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

This paper discusses the issues and approaches involved in developing a mobile vehicle-mounted system to detect, classify, and log the surrounding vehicles in a database for efficient query-based retrieval. This system consists of three components: (1) vehicle sensing, detection, and tracking (2) feature extraction and classification (3) database storage and retrieval. Relevant research in each of these components is described in order to guide the development of such a system. Such a system has applications including traffic analysis from mobile probes, analyzing driver behavior based on surrounding vehicles, as well as surveillance from mobile platform.

关键词： Vehicle detection Cameras Road vehicles Information retrieval Image sensors Vehicle driving Object detection Monitoring Layout Laser radar

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A Two-stage Multi-view Analysis Framework for Human Activity and Interactions

A Two-stage Multi-view Analysis Framework for Human Activity...

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Workshop on Motion and Video Computing

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

ISBN: (纸本)9781424431366;9780769527932

This paper presents a new framework for a multi-stage multi-view approach for human interactions and activity analysis. The analysis is performed in a distributed vision system that synergistically integrate track- and body-level representations across multiple cameras. Our system aims at versatile and easily-deployable system that does not require careful camera calibration. Main contributions of the paper are: (1) context-dependent camera handover for occlusion handling, (2) switching the multi-stage analysis between track- and body-level representations, and (3) a hypothesis-verification paradigm for top-down feedback exploiting spatio-temporal constraints inherent in human interaction. Experimental evaluation shows the efficacy of the proposed system for analyzing multi-person interactions. Current implementation uses two views, but extension to more views is straightforward.

关键词： Humans Cameras computer vision Condition monitoring Robot vision systems Machine vision Feedback Motion analysis computerized monitoring Image analysis

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Homography-based Analysis of People and Vehicle Activities in Crowded Scenes

Homography-based Analysis of People and Vehicle Activities i...

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IEEE Workshop on Applications of computer vision (WACV)

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

This paper presents an new framework for homography-based analysis of pedestrian-vehicle activity in crowded scenes. Planar homography constraint is exploited to extract view-invariant object features including footage area and velocity of objects on the ground plane. Spatio-temporal relationships between people- and vehicle- tracks are represented by a semantic event. Context awareness of the situation is achieved by the estimated density distribution of objects and the anticipation of possible directions of near-future tracks using piecewise velocity history. Single-view and multi-view based homography mapping options are compared. Our framework can be used to enhance situational awareness for disaster prevention, human interactions in structured environments, and crowd movement analysis at wide regions

关键词： Vehicles Layout Surveillance Humans computer vision Cameras Robot vision systems Safety Transmission line matrix methods Feature extraction

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Real-Time Video Based Highway Traffic Measurement and Performance Monitoring

Real-Time Video Based Highway Traffic Measurement and Perfor...

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International Conference on Intelligent Transportation

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

This paper presents a real-time highway monitoring system for tracking and classification of vehicles with the computation of traffic flow parameters from live video streams. The proposed system robustly detects and tracks vehicles during daylight hours and accurately classifies them into 8 different types by leveraging tracking information. The system is able to process video continuously over long time periods, accumulating large volumes of tracking data to build daily highway models consisting of the traffic flow parameters, density, flow, and speed. These daily models are used to categorize the speed profile of live traffic.

关键词： Road transportation Traffic control Detectors Vehicle detection Road vehicles Object detection Event detection Real time systems Condition monitoring computer vision

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A Comparison of Color and Infrared Stereo Approaches to Pedestrian Detection

A Comparison of Color and Infrared Stereo Approaches to Pede...

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IEEE Symposium on Intelligent Vehicle

作者： Stephen J. Krotosky Mohan M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA USA

This paper presents an analysis of color and infrared stereo approaches to pedestrian detection. We design a four camera experimental testbed consisting of two color and two infrared cameras that allows for synchronous capture and direct frame-by-frame comparison of pedestrian detection approaches. We incorporate this four camera system in a test vehicle and conduct comparative experiments of stereo-based approaches to obstacle detection using color and infrared imagery. A detailed analysis of these experiments shows the robustness of both color and infrared stereo imagery to generate the dense stereo maps necessary for robust object detection and motivates investigation of color and infrared features that can be used to further classify detected obstacles into pedestrian regions. The complementary nature of color and infrared features gives rise to a discussion of a feature fusion techniques, including a cross-spectral stereo solution to pedestrian detection.

关键词： Infrared detectors Image color analysis Cameras Infrared imaging Robustness Object detection System testing Vehicle detection Image analysis Fusion power generation

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