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检索条件"机构=Computer Vision and Robotics Research LaboratoryLa Jolla"

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Attention estimation by simultaneous observation of viewer and view

Attention estimation by simultaneous observation of viewer a...

引用

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

作者： Anup Doshi Mohan M. Trivedi Computer Vision and Robotics Research Lab University of California San Diego La Jolla CA USA

We introduce a new approach to analyzing the attentive state of a human subject, given cameras focused on the subject and their environment. In particular, the task of analyzing the focus of attention of a human driver is of primary concern. Up to 80% of automobile crashes are related to driver inattention; thus it is important for an Intelligent Driver Assistance System (IDAS) to be aware of the driver state. We present a new Bayesian paradigm for estimating human attention specifically addressing the problems arising in dynamic situations. The model incorporates vision-based gaze estimation, “top-down”- and “bottom-up”-based visual saliency maps, and cognitive considerations such as inhibition of return and center bias that affect the relationship between gaze and attention. Results demonstrate the validity on real driving data, showing quantitative improvements over systems using only gaze or only saliency, and elucidate the value of such a model for any human-machine interface.

关键词： Humans Layout State estimation Bayesian methods Vehicle dynamics Robot vision systems Man machine systems Probability distribution Computational modeling Observers

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Vehicle Iconic Surround Observer: Visualization platform for intelligent driver support applications

Vehicle Iconic Surround Observer: Visualization platform for...

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IEEE Symposium on Intelligent Vehicle

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

With the introduction of intelligent driver support systems, vehicles have become more comfortable and safer. But, these systems require new sensors and the information they contain must be efficiently presented to the driver. The cognitive demands for interpreting these signals may prove to be a distraction with negative impact on driving performance. This work describes a unified visualization scheme, the Vehicle Iconic Surround Observer, capable of introducing new surround sensors into a common display environment which quickly conveys critical surround context with minimal driver interpretation.

关键词： Vehicle driving Visualization Intelligent vehicles Intelligent sensors Vehicle safety Mechanical sensors Lenses Intelligent robots Laboratories Intelligent systems

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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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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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An adaptive scene description for activity analysis in surveillance video

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

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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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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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Registration of Multimodal Stereo Images Using Disparity Voting from Correspondence Windows

Registration of Multimodal Stereo Images Using Disparity Vot...

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

作者： Stephen Krotosky Mohan Trivedi Computer Vision and Robotics Research LaboratoryLa Jolla University of California San Diego CA USA

This paper presents a method for registering multimodal imagery in short range surveillance situations when the differences in object depths preclude any global registration techniques. An analysis of multimodal registration approaches gives insight into the limitations of global assumptions and motivates the developed algorithm. Using calibrated stereo imagery, we use maximization of mutual information in sliding correspondence windows that inform a disparity voting scheme to demonstrate successful registration of color and thermal images. Extensive testing of scenes with multiple people at different depths and levels of occlusion shows high rates of successful registration and gives a reliable framework for further processing and analysis of the multimodal imagery.

关键词： Voting Cameras Layout Surveillance Mutual information Image registration Stereo vision Image analysis Information analysis Object detection

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Panoramic Appearance Map (PAM) for Multi-camera Based Person Re-identification

Panoramic Appearance Map (PAM) for Multi-camera Based Person...

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

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

This paper proposes a concept of panoramic appearance map to perform reidentification of a people who leave the scene and reappear after some time. The map is a compact signature of appearance information of a person extracted from multiple cameras. The person is detected and tracked in multiple cameras and triangulation is used to accurately localize the person in 3-D. A virtual cylinder is formed around the person's location and mapped onto an image with the horizontal axis representing the azimuth angle and vertical axis representing the height. Each bin in the map image gets the appearance information from all the cameras which can observe it. The maps between different tracks are matched using a weighted metric. Experimental results showing person matching and reidentification show the effectiveness of the approach.

关键词： Cameras Data mining Azimuth Layout Surveillance Intelligent vehicles Color Histograms computer vision Robot vision systems

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Multimodal Voxelization and Kinematically Constrained Gaussian Mixture Models for Full Hand Pose Estimation: An Integrated Systems Approach

Multimodal Voxelization and Kinematically Constrained Gaussi...

引用

IEEE International Conference on computer vision Systems (ICVS)

作者： Shinko Y. Cheng M.M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA USA

We propose a new approach to the hand pose estimation problem using only volume information. We describe a thermal and color image-based approach to generate silhouettes of the hand with which voxel images are produced using shape-from-silhouette. We assume a 16-component, 27 degree-of-freedom kinematically constrained Gaussian mixture model, and fit it over the voxel images. We constrain the otherwise freely-arranged components of this model by a system of equations describing joint characteristics parameterized by component centroids and orientations. We use the EM algorithm and steepest descent to estimate the parameters of the model such that they yield the maximum likelihood and kinematically correct pose estimates. We demonstrate the effectiveness of the proposed system on synthesized as well as captured voxel images of the hand, and show that given appropriate initial conditions, the iterative model parameter estimation procedure effectively converges to and tracks the "voxelized" articulated hand.

关键词： Joints Biological system modeling Image segmentation Image reconstruction computer vision Image generation Parameter estimation Yield estimation Robot vision systems Laboratories

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