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

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Detection and filtering of landmark occlusions using Terrain Spatiograms

Detection and filtering of landmark occlusions using Terrain...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Damian M. Lyons Robotics & Computer Vision Laboratory Computer and Information Science Department Fordham University Bronx NY USA

A team of robots cooperating to quickly produce a map needs to share landmark information between members so that the local maps can be accurately merged. However, the appearance of landmarks as seen by members of the team can change dramatically due to the phenomenon of occlusion. We have previously presented an approach to landmark representation using Terrain Spatiograms an extension to image spatiograms in which the spatial information relates to the scene rather than the image. Because this representation preserves depth structure, it is possible to identify and filter potential occlusions. We present an approach to identifying and filtering occlusions using terrain spatiograms, and report experimental results on 20 landmark datasets for varying states of occlusion. We show that occlusion can be detected and filtered, resulting in improved landmark matching scores.

关键词： Robots Histograms robotics and automation Matched filters Hazards USA Councils Information filtering Information filters Layout LAN interconnection

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Vanishing point detection for CCTV in railway stations

Vanishing point detection for CCTV in railway stations

引用

5th International Conference on Imaging for Crime Detection and Prevention, ICDP 2013

作者： Tarrit, K. Atkinson, G.A. Smith, M.L. Molleda, J. Wright, G.C. Gaal, P. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Department of Computer Science and Engineering University of Oviedo Spain Aralia Systems Ltd. Horsham United Kingdom

ISBN: (纸本)9781849199049

Vanishing point (VP) detection is an important task in computer vision with particular importance in video surveillance. However, despite having numerous applications in camera calibration, 3D reconstruction and threat detection, a general method for VP detection has remained elusive. Rather than attempting the infeasible task of VP detection in general scenes, this paper presents a novel method that is fine-tuned to work for railway station environments and is shown to outperform the state-of-the-art for that particular case. The motivation for this is that (a) these environments are particularly susceptible to many types of crime from petty theft to terrorist activity, (b) the number of objects/structures in the scenes are limited, rendering the problem more tractable than the generic case, and (c) they typically have many CCTV cameras already installed. The method presented here commences by extracting edges from the input frame using the Canny edge detector as a pre-processing stage before the standard Hough transform is employed. A novel line clustering algorithm is then applied to determine the parameters of the lines that converge at VPs. This is based on statistics of the detected lines and heuristics about the type of scene. The clustered lines are then used to compute VPs using their intersection points. A voting system is used to optimise detection in an attempt to omit spurious lines. The paper includes a direct comparison to the state-of-the-art and ground truth.

关键词： Subway stations

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Feasible RRT-based path planning using seventh order Bézier curves

Feasible RRT-based path planning using seventh order Bézier...

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2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Armando A. Neto Douglas G. Macharet Mario F. M. Campos Computer Vision and Robotics Laboratory (Verlab) Computer Science Department Federal University of Minas Gerais Brazil

This paper presents a methodology based on a variation of the Rapidly-exploring Random Trees (RRTs) that generates feasible trajectories for autonomous vehicles with holonomic constraints in environments with obstacles. Our approach is based on seventh order Bézier curves to connect vertexes of the tree, generating paths that do not violate the main kinematic constraints of the vehicle. The methodology also does not require complex kinematic and dynamic models of the vehicle. The smoothness of the acceleration profile of the entire path is directly guaranteed by controlling the curvature values at the extreme points of each Bézier that composes the tree. The proposed algorithm provides fast convergence to the final result with several other advantages, such as the reduction in the number of vertexes of the tree because the method enable connections between vertexes of the tree with unlimited range. In an environment with few obstacles, a very small quantity of vertexes (sometimes only two) is sufficient to take the robot between two points. The properties of the seventh order Bézier formulation are also used to avoid collisions with static obstacles in the environment.

关键词： Vehicles Robots Vehicle dynamics Mathematical model Kinematics Trajectory

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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...

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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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Stereo Matching with Symmetric Cost Functions

Stereo Matching with Symmetric Cost Functions

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Kuk-Jin Yoon In So Kweon Robotics and Computer Vision Laboratory Department Electrical Engineering and Computer Science KAIST Daejeon South Korea

Recently, many global stereo methods have achieved good results by modeling a disparity surface as a Markov random field (MRF) and by solving an optimization problem with various techniques. However, most global methods mainly focus on how to minimize conventional cost functions efficiently, although it is more important to define cost functions well to improve performance. In this paper, we propose new symmetric cost functions for global stereo methods. We first present a symmetric data cost function for the likelihood and then propose a symmetric discontinuity cost function for the prior in the MRF model for stereo. In defining cost function, both the reference image and the target image are taken into account to improve performance without modeling half-occluded pixels explicitly and without using color segmentation. The performance improvement of stereo matching due to the proposed symmetric cost functions is verified by applying the proposed symmetric cost functions to the belief propagation (BP) based stereo method. Experimental results for standard testbed images show that the performance of the BP based stereo method is greatly improved by the proposed symmetric cost functions.

关键词： Cost function Stereo vision Pixel computer vision Markov random fields Optimization methods Image segmentation Belief propagation Robot vision systems computer science

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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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Deep representation of industrial components using simulated images

Deep representation of industrial components using simulated...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Seong-Heum Kim Gyeongmin Choe Byungtae Ahn In So Kweon Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Republic of Korea

In this paper, we present a visual learning framework to retrieve a 3D model and estimate its pose from a single image. To increase the quantity and quality of training data, we define our simulation space in the near infrared (NIR) band, and utilize the quasi-Monte Carlo (MC) method for scalable photorealistic rendering of manufactured components. Two types of convolutional neural network (CNN) architectures are trained over these synthetic data and a relatively small amount of real data. The first CNN model seeks the most discriminative information and uses it to classify industrial components with fine-grained shape attributes. Once a 3D model is identified, one of the category-specific CNNs is tested for pose regression in the second phase. The mixed data for learning object categories is useful in domain adaptation and attention mechanism in our system. We validate our data-driven method with 88 component models, and the experimental results are qualitatively demonstrated. Also, the CNNs trained with various conditions of mixed data are quantitatively analyzed.

关键词： Three-dimensional displays Rendering (computer graphics) Computational modeling Solid modeling Shape Cameras Adaptation models

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Robust Reference-Based Super-Resolution With Similarity-Aware Deformable Convolution

Robust Reference-Based Super-Resolution With Similarity-Awar...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Gyumin Shim Jinsun Park In So Kweon Robotics and Computer Vision Laboratory Korea Advanced Institute of Science and Technology Republic of Korea

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

In this paper, we propose a novel and efficient reference feature extraction module referred to as the Similarity Search and Extraction Network (SSEN) for reference-based super-resolution (RefSR) tasks. The proposed module extracts aligned relevant features from a reference image to increase the performance over single image super-resolution (SISR) methods. In contrast to conventional algorithms which utilize brute-force searches or optical flow estimations, the proposed algorithm is end-to-end trainable without any additional supervision or heavy computation, predicting the best match with a single network forward operation. Moreover, the proposed module is aware of not only the best matching position but also the relevancy of the best match. This makes our algorithm substantially robust when irrelevant reference images are given, overcoming the major cause of the performance degradation when using existing RefSR methods. Furthermore, our module can be utilized for self-similarity SR if no reference image is available. Experimental results demonstrate the superior performance of the proposed algorithm compared to previous works both quantitatively and qualitatively.

关键词： Feature extraction Convolution Image reconstruction Image resolution Degradation Robustness Kernel

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Camera Exposure Control for Robust Robot vision with Noise-Aware Image Quality Assessment

Camera Exposure Control for Robust Robot Vision with Noise-A...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ukcheol Shin Jinsun Park Gyumin Shim Francois Rameau In So Kweon Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Republic of Korea

In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture best-exposed images, which can boost the performance of various computer vision and robotics tasks. For this purpose, we carefully design an image quality metric that captures complementary quality attributes and ensures light-weight computation. Specifically, our metric consists of a combination of image gradient, entropy, and noise metrics. The synergy of these measures allows the preservation of sharp edges and rich texture in the image while maintaining a low noise level. Using this novel metric, we propose a real-time and fully automatic exposure and gain control technique based on the Nelder-Mead method. To illustrate the effectiveness of our technique, a large set of experimental results demonstrates the higher qualitative and quantitative performance compared with conventional approaches.

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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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