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检索条件"机构=The Computer Vision And Robotics Research Laboratory"

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Pedestrian detection based on LIDAR-driven sliding window and relational parts-based detection

Pedestrian detection based on LIDAR-driven sliding window an...

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IEEE Symposium on Intelligent Vehicle

作者： Luciano Oliveira Urbano Nunes Intelligent Vision Research Laboratory Federal University Bahia Brazil Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal

The most standard image object detectors are usually comprised of one or multiple feature extractors or classifiers within a sliding window framework. Nevertheless, this type of approach has demonstrated a very limited performance under datasets of cluttered scenes and real life situations. To tackle these issues, LIDAR space is exploited here in order to detect 2D objects in 3D space, avoiding all the inherent problems of regular sliding window techniques. Additionally, we propose a relational parts-based pedestrian detection in a probabilistic non-iid framework. With the proposed framework, we have achieved state-of-the-art performance in a pedestrian dataset gathered in a challenging urban scenario. The proposed system demonstrated superior performance in comparison with pure sliding-window-based image detectors.

关键词： Detectors Laser radar Feature extraction Support vector machines Training Probabilistic logic Vectors

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Learning multi-lane trajectories using vehicle-based vision

Learning multi-lane trajectories using vehicle-based vision

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International Conference on computer vision Workshops (ICCV Workshops)

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

Safe operation of a motor vehicle requires awareness of the current traffic situation as well as the ability to predict future maneuvers. In order to provide an intelligent vehicle the ability to make predictions, this work proposes a framework for understanding the driving situation based on vehicle mounted vision sensors. Vehicles are tracked using Kalman filtering based on a vision-based system that detects and tracks using a combination of monocular and stereo-vision. The vehicles' full trajectories are recorded, and a data-driven learning framework has been applied to automatically learn surround behaviors. By learning based on observations, the ADAS system is being trained by experience. Learned trajectories have been compared between dense and free-flowing traffic conditions. Preliminary experimental results using real-world multi-lane highways show the basic promise of this approach. Future research directions are discussed.

关键词： Vehicles Trajectory Hidden Markov models Mathematical model Detectors Equations Training

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Lidar based off-road negative obstacle detection and analysis

Lidar based off-road negative obstacle detection and analysi...

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International Conference on Intelligent Transportation

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

In order for an autonomous unmanned ground vehicle (UGV) to drive in off-road terrain at high speeds, it must analyze and understand its surrounding terrain in realtime: it must know where it intends to go, where are the hazards, and many details of the topography of the terrain. Much research has been done in the way of obstacle avoidance, terrain classification, and path planning, but still so few UGV systems can accurately traverse off-road environments at high speeds autonomously. One of the most dangerous hazards found off-road are negative obstacles, mainly because they are so difficult to detect. We present algorithms that analyze the terrain using a point cloud produced by a 3D laser range finder, then attempt to classify the negative obstacles using both a geometry-based method we call the Negative Obstacle DetectoR (NODR) as well as a support vector machine (SVM) algorithm. The terrain is analyzed with respect to a large UGV with the sensor mounted up high as well as a small UGV with the sensor mounted low to the ground.

关键词： Laser radar Vehicles Support vector machines Robot sensing systems Lasers Three dimensional displays Equations

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Retinal vessel tortuosity evaluation via Circular Hough Transform

Retinal vessel tortuosity evaluation via Circular Hough Tran...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Farnoosh Ghadiri HamidReza Pourreza Touka Banaee Morteza Delgir Computer Engineering Department Machine Vision Laboratory Ferdowsi University of Mashhad Iran Computer Engineering Department Machine Vision Laboratory University of Ferdowsi Mashhad Iran Ophthalmic Research Center Khatam-Al-Anbia Hospital Medical science University of Mashhad Iran Computer Science Department Robotics Laboratory Northeastern University USA

Retinal vessel tortuosity has shown to be significantly associated with cardiovascular diseases such as hypertension and diabetes. Despite importance of this field a few techniques have been proposed yet. All previous methods rely on a vessel extraction phase, which its accuracy affects final output and also time consuming. Nobility of presented algorithm is to introduce a method for evaluating retinal vessel tortuosity without any explicit vessel detection. We use the Circular Hough Transform (CHT) based on gradient field of the retinal image. Each vessel curve is detected as a semi-circle by Hough transform and tortuosity of the curve is determined with the help of accumulated value of circle center and its radius. As there are no any specific database for tortuosity evaluation, the algorithm was tasted on database consisting of 40 images, mixture of DRIVE database and images from Khatam-Al-Anbia Hospital consisting of 40 retinal images, of which 20 were tortuous and 20 were non-tortuous. The proposed algorithm can achieve classification rate of 92% along with less computation time in compare of previous methods.

关键词： Transforms Retinal vessels Classification algorithms Biomedical measurements Biomedical imaging Image segmentation

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Contextual activity visualization from long-term video observations

Contextual activity visualization from long-term video obser...

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作者： Morris, Brendan Tran Trivedi, Mohan Manubhai Computer Vision and Robotics Research Laboratory University of California San Diego United States

The Contextual Activity Notification Visualization Analysis System (Canvas) provides a user interaction interface for instantaneous feedback of contextual processing units that enable high-level semantic extraction an... 详细信息

关键词： Visualization

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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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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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Self-organized executive control functions

Self-organized executive control functions

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International Joint Conference on Neural Networks (IJCNN)

作者： Michail Maniadakis Panos Trahanias Jun Tani Computational Vision and Robotics Laboratory Institute of Computer Science Foundation for Research and Technology Hellas Crete Greece Laboratory for Behavior and Dynamic Cognition RIKEN Brain Science Institute Wako Saitama Japan

Executive control incorporates cognitive functions involved in the control and management of other cognitive processes. Such high-level skills are hard to be explored with brain imaging studies because they require complex and persistent experimental procedures. Alternatively, computational modeling may provide a new way to indirectly explore executive control mechanisms. The current work adopts this latter approach to explore possible characteristics of executive control, focusing particularly on behavioral rule switching and confidence neurodynamics in artificial agents. To this end, our study explores a robotic version of the classical Wisconsin Card Sorting Test, incorporating also the option of betting. Our ability to perform multiple and statistically independent computational experiments together with the in-depth study of the mechanisms created in the artificial cognitive systems, provides suggestions for the executive control aspects of the human brain.

关键词： Switches Robot sensing systems Brain modeling Neurons Encoding

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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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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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