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Sensor Fusion: Spatial Reasoning and Scene Interpretation

作者： Aubry, Stéphane Hayward, Vincent Computer Vision and Robotics Laboratory McGill Research Center for Intelligent Machines McGill University MontréalQC Canada

ISBN: (纸本)9780819400383

We consider the problem of aggregating the information provided by sensors such as range finders and its applications to scene modelling. This information consists of collections of three-dimensional points which form a discrete subspace of the objects-to-free-space boundaries within the world to be modelled. The ultimate goal is to obtain a valid surface model which can in turn be transformed into an efficient volumetric representation for solid interference detection algorithms. We first relate our work to that of other researchers, and we then show that surface connectivity is a non-trivial but essential element of a valid representation. To that end, we introduce a formal definition for model validity which we use to guide the process of aggregating the different views and we provide a proof of its correctness under stated assumptions. We eventually suggest heuristic methods for extending our approach when the strict validity conditions do not suffice to construct a perceptually consistent model. Finally, we quickly introduce the topological problems posed by objects non-homeomorphic to spheres, such as multi-holed tori. The methods we develop do not depend on the physical nature of the sensing technique. For example, our results can also be used from data obtained by stereo analysis and to some extent by haptic sensing.

关键词： Heuristic methods

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Dynamic panoramic surround map: Motivation and omni video based approach

Dynamic panoramic surround map: Motivation and omni video ba...

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2005 IEEE computer Society Conference on computer vision and Pattern Recognition, CVPR 2005 - Workshops

作者： Gandhi, Tarak Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory University of California San Diego San DiegoCA92093 United States

ISBN: (纸本)0769526608

Awareness of what surrounds a vehicle directly affects the safe driving and maneuvering of an automobile. Surround information or maps can help in ethnographic studies of driver behavior as well as provide a critical input in the development of effective driver assistance system. In this paper, we introduce the concept of Dynamic Panoramic Surround (DPS) map that shows the nearby surroundings of the vehicle, and detects the objects of importance on the road. Omnidirectional cameras which give a panoramic view of the surroundings can be useful for visualizing and analyzing the nearby surroundings of the vehicle. A novel approach for synthesizing the DPS using stereo and motion analysis of video images from a pair of omni-directional cameras on the vehicle is developed. Successful generation of DPS in experimental runs on an instrumented vehicle testbed is demonstrated. These experiments prove the basic feasibility and show promise of omni video based DPS capture algorithm to provide useful semantic descriptors of the state of moving vehicles and obstacles in the vicinity of a vehicle. © 2005 IEEE computer Society. All rights reserved.

关键词： Object detection

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Multiperspective thermal IR and video arrays for 3D body tracking and driver activity analysis

Multiperspective thermal IR and video arrays for 3D body tra...

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2005 IEEE computer Society Conference on computer vision and Pattern Recognition, CVPR 2005 - Workshops

作者： Cheng, Shinko Y. Park, Sangho Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego San DiegoCA United States

ISBN: (纸本)0769526608

This paper presents a multi-perspective (i.e., four camera views) multi-modal (i.e., thermal infrared and color) video based system for robust and real-time 3D tracking of important body *** multi-perspective characteristics of the system provides 3Dtrajectory of the body parts, while the multi-modal characteristics of the system provides robustness and reliability of feature detection and tracking. The application context for this research is that of intelligent vehicles and driver assistance systems. Experimental results demonstrate effectiveness of the proposed system. © 2005 IEEE computer Society. All rights reserved.

关键词： Automobile drivers

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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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Multimodal voxelization and kinematically constrained Gaussian mixture models for full hand pose estimation: An integrated systems approach

Multimodal voxelization and kinematically constrained Gaussi...

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Fourth IEEE International Conference on computer vision Systems, ICVS'06

作者： Cheng, Shinko Y. Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory University of California San Diego 9500 Oilman Drive San Diego CA 92037

ISBN: (纸本)0769525067

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. © 2006 IEEE.

关键词： Motion estimation

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Distributed interactive video arrays for event based analysis of incidents 5

Distributed interactive video arrays for event based analysi...

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5th IEEE International Conference on Intelligent Transportation Systems, ITSC 2002

作者： Trivedi, Mohan M. Prati, Andrea Kogut, Greg Computer Vision and Robotics Research Laboratory University of California at San Diego San DiegoCA92093-0434 United States

ISBN: (纸本)0780373898

The Distributed Interactive Video Array (DIVA) system is developed to provide a large-scale, redundant cluster of video streams to observe a remote scene and to supply automatic focus of-attentian with event-driven serving to capture desired events at appropriate resolutions and perspectives. Installing multiple sensors introduces several new research issues related to the system design, including handoff schemes for passing tracked objects between sensors and clusters, methods for determining the "best view" given the context of the traffic scene, and sensor fusion algorithms to best employ the strengths of a given sensor or sensor modality. This paper describes our research focused on the development of DIVA system for traffic and incident monitoring. The paper describes the overall architecture of the DIVA system. Algorithms (or vehicle and platoon tracking using multiple cameras, and experimental results using novel distributed video networks deployed on the campus and the interstate 1-5. © 2002 IEEE.

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Robust face recognition using key-point descriptors 10

Robust face recognition using key-point descriptors

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10th International Conference on computer vision Theory and Applications, VISAPP 2015

作者： Klemm, Soeren Andreu, Yasmina Henriquez, Pedro Matuszewski, Bogdan J. Robotics and Computer Vision Research Laboratory School of Computing Engineering and Physical Sciences University of Central Lancashire Preston United Kingdom

ISBN: (纸本)9789897580901

Key-point based techniques have demonstrated a good performance for recognition of various objects in numerous computer vision applications. This paper investigates the use of some of the most popular key-point descriptors for face recognition. The emphasis is put on the experimental performance evaluation of the key-point based face recognition methods against some of the most popular and best performing techniques, utilising both global (Eigenfaces) and local (LBP, Gabor filters) information extracted from the whole face image. Most of the results reported in literature so far, on the use of the key-points descriptors for the face recognition, concluded that the methods based on processing of the full face image have somewhat better performances than methods using exclusively key-points. The results reported in this paper suggest that the performance of the key-point based methods could be at least comparable to the leading "whole face" methods and are often better suited to handle face recognition in practical applications, as they do not require face image co-registration, and perform well even with significantly occluded faces.

关键词： Face recognition

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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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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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Unsupervised learning of motion patterns of rear surrounding vehicles

Unsupervised learning of motion patterns of rear surrounding...

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2009 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2009

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)9781424454419

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. ©2009 IEEE.

关键词： Vehicles

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