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

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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...

引用

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 hybrid to range image detection

A hybrid to range image detection

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International Conference on Pattern Recognition

作者： N. Yokoya M. Levine Electro Technical Laboratory Tsukuba Ibaraki Japan Computer Vision and Robotics Laboratory Computer Vision and Robotics Laboratory McGill Research Centre for Intelligent Machines McGill University Montreal QUE Canada

An approach to the problem of range image segmentation is described. It is a hybrid of region- and edge-based approaches. It is assumed that the range image of an object, which may be constructed of both curved and planar surfaces, is divided into surface primitives which are homogeneous in their intrinsic differential geometric properties and do not contain discontinuities in either depth or surface orientation. The method is based on the computation of the Gaussian and mean curvatures from which a curvature sign map is computed. Two initial edge-based segmentations are also computed from the partial derivatives and depth values. One detects jump edges while the other highlights roof edges. The three image maps are then combined to produce the final range image segmentation. Experimental results for both synthetic and real range data of polyhedral and curved objects have proved the usefulness of the approach. Results for the real data are given.< >

关键词： Surface fitting Smoothing methods Shape Image analysis Pixel Geometry

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Improving stereo camera depth measurements and benefiting from intermediate results

Improving stereo camera depth measurements and benefiting fr...

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IEEE Symposium on Intelligent Vehicle

作者： C. Høilund T. B. Moeslund C. B. Madsen M. M. Trivedi Laboratory of Computer Vision and Media Technology Aalborg University Denmark Computer Vision and Robotics Research Laboratory University of California San Diego USA

This paper presents a method for improving the disparity values obtained with a stereo camera by applying an iconic Kalman filter and known ego-motion. The improvements are demonstrated in an application of determining the free space in a scene as viewed by a vehicle-mounted camera. Using disparity maps from a stereo camera and known camera motion, the disparity maps are first filtered by an iconic Kalman filter, operating on each pixel individually, thereby reducing variance and increasing the density of the filtered disparity map. Then, a stochastic occupancy grid is calculated from the filtered disparity map, providing a top-down view of the scene where the uncertainty of disparity measurements are taken into account. These occupancy grids are segmented to indicate a maximum depth free of obstacles, enabling the marking of free space in the accompanying intensity image. Even without motion of the camera, the quality of the disparity map is increased significantly. Applications of the intermediate results are discussed, enabling features such as motion detection and quantifying the certainty of the measurements. The evaluation shows significant improvement in disparity variance and disparity map density, and consequently an improvement in the application of marking free space.

关键词： Filters Stereo vision Intelligent vehicles Robot vision systems Layout USA Councils Smart cameras Laboratories computer vision Motion detection

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Learning trajectory patterns by clustering: Experimental studies and comparative evaluation

Learning trajectory patterns by clustering: Experimental stu...

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Conference on computer vision and Pattern Recognition (CVPR)

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

Recently a large amount of research has been devoted to automatic activity analysis. Typically, activities have been defined by their motion characteristics and represented by trajectories. These trajectories are collected and clustered to determine typical behaviors. This paper evaluates different similarity measures and clustering methodologies to catalog their strengths and weaknesses when utilized for the trajectory learning problem. The clustering performance is measured by evaluating the correct clustering rate on different datasets with varying characteristics.

关键词： Principal component analysis Euclidean distance computer vision Prototypes Hidden Markov models Robot vision systems robotics and automation Laboratories Pattern analysis Layout

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A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation 37

A Single 2D Pose with Context is Worth Hundreds for 3D Human...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Zhao, Qitao Zheng, Ce Liu, Mengyuan Chen, Chen Robotics Institute Carnegie Mellon University United States Center for Research in Computer Vision University of Central Florida United States Key Laboratory of Machine Perception Peking University Shenzhen Graduate School China

ISBN: (纸本)9781713899921

The dominant paradigm in 3D human pose estimation that lifts a 2D pose sequence to 3D heavily relies on long-term temporal clues (i.e., using a daunting number of video frames) for improved accuracy, which incurs performance saturation, intractable computation and the non-causal problem. This can be attributed to their inherent inability to perceive spatial context as plain 2D joint coordinates carry no visual cues. To address this issue, we propose a straightforward yet powerful solution: leveraging the readily available intermediate visual representations produced by off-the-shelf (pre-trained) 2D pose detectors - no finetuning on the 3D task is even needed. The key observation is that, while the pose detector learns to localize 2D joints, such representations (e.g., feature maps) implicitly encode the joint-centric spatial context thanks to the regional operations in backbone networks. We design a simple baseline named Context-Aware PoseFormer to showcase its effectiveness. Without access to any temporal information, the proposed method significantly outperforms its context-agnostic counterpart, PoseFormer [74], and other state-of-the-art methods using up to hundreds of video frames regarding both speed and precision. Project page: ***/ContextAware-PoseFormer. © 2023 Neural information processing systems foundation. All rights reserved.

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3D Shape Context Based Gesture Analysis Integrated with Tracking using Omni Video Array

3D Shape Context Based Gesture Analysis Integrated with Trac...

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

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

In this paper we introduce a multilayer cylindrical histogram based feature space for representing 3D shape context of human body. Dynamics of gestures are analyzed using discrete hidden Markov models (DHMM) with quantized feature vectors. Extensive experimental trials with multiple subjects and a range of gesture classes are presented. Gesture recognition accuracies of over 85% (for nine gestures, and 9 subjects) and over 95% (for seven gestures) support the basic feasibility and robustness of the approach.

关键词： Shape Humans Hidden Markov models Biological system modeling Cameras Robustness Context modeling Information analysis Pattern analysis computer vision

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

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

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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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An integrated, robust approach to lane marking detection and lane tracking

An integrated, robust approach to lane marking detection and...

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IEEE Symposium on Intelligent Vehicle

作者： J.C. McCall M.M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego CA USA

Lane Detection is a difficult problem because of the varying road conditions that one can encounter while driving. We propose a method for lane detection using steerable filters. Steerable filters provide robustness to lighting changes and shadows and perform well in picking out both circular reflector road markings as well as painted line road markings. The filter results are then processed to eliminate outliers based on the expected road geometry and used to update a road and vehicular model along with data taken internally from the vehicles. Results are shown for a 9000-frame image sequence that include varying lane markings, lighting conditions, showing, and occlusion by other vehicles.

关键词： Robustness Filters Road vehicles Remotely operated vehicles Road transportation Vehicle detection Detectors computer vision Vehicle driving Mobile robots

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Design of an instrumented vehicle test bed for developing a human centered driver support system

Design of an instrumented vehicle test bed for developing a ...

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IEEE Symposium on Intelligent Vehicle

作者： J.C. McCall O. Achler M.M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego CA USA

We introduce a new type of intelligent vehicle test-bed that is enabling new research in the field. This new test-bed is designed to capture not just a portion of the vehicle surround, but rather the entire vehicle surround as well as the vehicle interior and vehicle state for extended periods of time. This is accomplished using multiple modalities of sensor systems so that it conform a complete context of the vehicle. This allows new research to be performed in intelligent vehicle algorithm development and allows studies in driver behavior to be performed. We also show results from some of the research being performed using this test-bed.

关键词： Instruments Vehicle driving System testing Humans Intelligent vehicles Sensor systems Automobiles Global Positioning System Remotely operated vehicles Laboratories

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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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