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检索条件"机构=Computer Vision and Robotics Research Laboratory"

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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...

引用

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

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

引用

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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Real-time wide area tracking: hardware and software infrastructure

Real-time wide area tracking: hardware and software infrastr...

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International Conference on Intelligent Transportation

作者： G.T. Kogut M.M. Trivedi The Computer Vision And Robotics Research Laboratory University of California La Jolla CA USA

In recent years, large number of cameras have been installed in freeway and road environments. While the use of some of these cameras is being automated through computer vision, few computer vision systems allow for true wide-area large scaled automation. This paper describes a distributed computing system capable of managing an arbitrarily large sensor network using only common computing and networking platforms. The architecture is capable of handling many common computer vision tasks, as well as the inter-sensor communication necessary for developing new algorithms which employ data from multiple sensors. This system is tested with an algorithm which tracks moving objects through a prototype camera network with non-overlapping fields or view in a college campus environment This algorithm allows the system to maintain the identity of a tracked objects as it leaves and enters the fields of view of individual sensors. Such an algorithm is necessary for applications which require tracking objects over large distances or over long periods of time in an environment without complete sensor coverage.

关键词： Hardware Cameras computer vision Distributed computing Sensor systems Traffic control Roads Automation computer network management Sensor phenomena and characterization

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Motion and depth perception using spatiotemporal frequency analysis

Motion and depth perception using spatiotemporal frequency a...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： G. Ravichandran M.M. Trivedi Electrical and Computer Engineering Department Computer Vision and Robrics Research Laboratory Computer Vision and Robotics Laboratary University of Tennessee Knoxville TN USA

Motion information is extracted by identifying the temporal signature associated with the textured objects in the scene. In this paper, we present a new computational framework for motion perception. Our methodology considers spatiotemporal frequency (STF) domain analysis to extract the optical flow information. First, we show that a sequence of image frames can be used to extract the motion parameters for the different regions in a dynamic scene using the basic Fourier transform properties in the STF analysis approach. When the observer (or the camera) moves, motion is induced in the scene, and the extracted motion information can then be used to estimate the depth parameters. A detailed analytical description of this model to interchangably extract motion and depth parameters and results to highlight their salient properties are presented.< >

关键词： Motion analysis Spatiotemporal phenomena Data mining Layout Image motion analysis Frequency domain analysis Information analysis Fourier transforms Image analysis Image sequence 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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Face detection and head tracking using stereo and thermal infrared cameras for "smart" airbags: a comparative analysis

Face detection and head tracking using stereo and thermal in...

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International Conference on Intelligent Transportation

作者： S.J. Krotosky S.Y. Cheng M.M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego CA USA

vision based systems for "smart" airbag systems aim to give precise information about occupant pose and location. This information can be used to make intelligent airbag deployment decisions. This paper reviews both a stereo-based and long wave infrared-based system for "smart" airbag deployment. The algorithms are systematically evaluated through extensive real-time occupant tests. Data for both algorithms is simultaneously collected and evaluated for viability in an intelligent airbag system. Results of these experimental trials show the feasibility of each video based occupant position analysis system. Advantages and drawbacks of each method are discussed and potential solutions are suggested.

关键词： Face detection Infrared detectors Smart cameras Lighting Information analysis Real time systems System testing Intelligent systems Machine vision Automobiles

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An adaptive scene description for activity analysis in surveillance video

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

引用

International Conference on Pattern Recognition

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

ISBN: (纸本)9781424421749

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.

关键词： Layout Surveillance Hidden Markov models Video compression Vocabulary Cameras computer vision Robot vision systems Laboratories Trajectory

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Vehicle mounted wide FOV stereo for traffic and pedestrian detection

Vehicle mounted wide FOV stereo for traffic and pedestrian d...

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IEEE International Conference on Image Processing

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

This paper describes an approach for detecting objects in front of an automobile using wide field of view stereo with a pair of omni cameras. Several configurations are suggested for effective detection of vehicles and pedestrians. The omni cameras are calibrated using sets of parallel lines on a parking lot. The calibration is used to rectify the omni images. Stereo matching is performed on the rectified images to detect other vehicles and pedestrians. Experimental results show promise of detecting these objects on the road.

关键词： Vehicle detection Cameras Roads Stereo vision Object detection computer vision Calibration Motion analysis Robot vision systems Intelligent vehicles

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Streaming face recognition using multicamera video arrays

Streaming face recognition using multicamera video arrays

引用

International Conference on Pattern Recognition

作者： K.S. Huang M.M. Trivedi Computer Vision and Robotics Research (CVRR) Laboratory University of California San Diego USA

We present face recognition schemes based on video streams: the majority decision rule and HMM maximum likelihood (ML) decision rules. PCA type of subspace feature analysis is first applied to the face images in a video segment of a fixed number of frames. The majority decision rule is then applied to PCA recognition results in the video segment. Discrete HMM (DHMM) is also applied to the single-frame recognition sequences. Continuous density HMM (CDHMM) is applied directly to the sequence of PCA feature vectors for ML decision on the video segment in a delayed decision manner. Experimental results are compared between these three schemes in terms of the number of states and Gaussian mixtures of the HMMs. CDHMM-based decision rule achieved a 99% correct recognition rate in average. A geometric interpretation of ML in the feature subspace well explains the observed performances.

关键词： Streaming media Face recognition Hidden Markov models Principal component analysis Image segmentation Lighting Testing computer vision Humans Covariance matrix

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computer vision for homeland security: a perspective on its promise and pitfalls

Computer vision for homeland security: a perspective on its ...

引用

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

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

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