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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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Multimodal Stereo Image Registration for Pedestrian Detection

Multimodal Stereo Image Registration for Pedestrian Detectio...

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International Conference on Intelligent Transportation

作者： S. Krotosky M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

This paper presents an approach for the registration of multimodal imagery for pedestrian detection when the significant depth differences of objects in the scene preclude a global alignment assumption. Using maximization-of-mutual-information matching techniques and sliding correspondence windows over calibrated image pairs, we demonstrate successful registration of color and thermal data. We develop a robust method using disparity voting for determining the registration of each object in the scene and provide a statistically based measure for evaluating the match confidence. Testing shows successful registration in complex scenes with multiple people at different depths and levels of occlusion

关键词： Image registration Layout Cameras Image color analysis Image analysis Voting Robustness Infrared imaging Infrared detectors Information analysis

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Facial Action Coding Using Multiple Visual Cues and a Hierarchy of Particle Filters

Facial Action Coding Using Multiple Visual Cues and a Hierar...

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

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

In this paper we present a framework for tracking nonrigid facial landmarks by combining various visual cues at multiple levels of detail. Using a probabilistic framework consisting of a hierarchy of particle filters, we are able to track individual facial landmarks using multiple visual cues at the local level, as well as tracking results at more coarse level of detail. This allows for the fusion of global and local cues in an efficient and robust manner. Testing is performed by tracking and classifying facial action codes obtained from the Cohn-Kanade AU-Coded Facial Expression Database.

关键词： Particle filters Face detection Robustness Particle tracking computer vision Intelligent vehicles Humans Magnetic heads Filtering Robot vision systems

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Biomimetic Centering for Undulatory Robots

Biomimetic Centering for Undulatory Robots

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： M. Sfakiotakis D.P. Tsakiris A. Vlaikidis Computational Vision and Robotics Laboratory Institute of Computer Science FORTH Heraklion Greece

Substantial work exists in the undulatory robotics literature on the mechanical design, modeling, gait generation and implementation of robotic prototypes. However, there appears to have been relatively limited work on the use of exteroceptive sensors in control schemes leading to more complex reactive undulatory behaviors. This paper considers a biologically-inspired sensor-based centering behavior for undulatory robots, originally developed for nonholonomic mobile robots. Adaptation to the significantly more complex dynamics of undulatory locomotors highlights a number of issues related to the use of sensors (possibly distributed over the elongated body of the mechanism) for the generation of reactive behaviors, to biomimetic neuromuscular control and to formation control of multi-undulatory swarms. These issues are explored via computational tools specifically geared towards undulatory locomotion in robotics and biology

关键词： Biomimetics Robot sensing systems Biosensors Mobile robots Biological system modeling Prototypes Mechanical sensors Neuromuscular Biology computing Computational biology

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Motion and depth perception using spatiotemporal frequency analysis

Motion and depth perception using spatiotemporal frequency a...

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IEEE International Conference on Systems, Man and Cybernetics

作者： G. Ravichandran M.M. Trivedi Electrical and Computer Engineering Department Computer Vision and Robrics Research Laboratory Computer Vision and Robotics Laboratary University of Tennessee Knoxville TN USA

Motion information is extracted by identifying the temporal signature associated with the textured objects in the scene. In this paper, we present a new computational framework for motion perception. Our methodology considers spatiotemporal frequency (STF) domain analysis to extract the optical flow information. First, we show that a sequence of image frames can be used to extract the motion parameters for the different regions in a dynamic scene using the basic Fourier transform properties in the STF analysis approach. When the observer (or the camera) moves, motion is induced in the scene, and the extracted motion information can then be used to estimate the depth parameters. A detailed analytical description of this model to interchangably extract motion and depth parameters and results to highlight their salient properties are presented.< >

关键词： Motion analysis Spatiotemporal phenomena Data mining Layout Image motion analysis Frequency domain analysis Information analysis Fourier transforms Image analysis Image sequence analysis

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Thermal Image Enhancement using Convolutional Neural Network

Thermal Image Enhancement using Convolutional Neural Network

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2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yukyung Choi Namil Kim Soonmin Hwang In So Kweon Dep. of EE Robotics and Computer Vision (RCV) Laboratory Daejeon Korea

With the advent of commodity autonomous mobiles, it is becoming increasingly prevalent to recognize under extreme conditions such as night, erratic illumination conditions. This need has caused the approaches using multi-modal sensors, which could be complementary to each other. The choice for the thermal camera provides a rich source of temperature information, less affected by changing illumination or background clutters. However, existing thermal cameras have a relatively smaller resolution than RGB cameras that has trouble for fully utilizing the information in recognition tasks. To mitigate this, we aim to enhance the low-resolution thermal image according to the extensive analysis of existing approaches. To this end, we introduce Thermal Image Enhancement using Convolutional Neural Network (CNN), called in TEN, which directly learns an end-to-end mapping a single low resolution image to the desired high resolution image. In addition, we examine various image domains to find the best representative of the thermal enhancement. Overall, we propose the first thermal image enhancement method based on CNN guided on RGB data. We provide extensive experiments designed to evaluate the quality of image and the performance of several object recognition tasks such as pedestrian detection, visual odometry, and image registration.

关键词： Image enhancement Cameras Image resolution Training Feature extraction Image reconstruction Robot vision systems

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An orientation assignment heuristic to the dubins traveling salesman problem

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8864卷 457-468页

作者： Macharet, Douglas G. Campos, Mario F.M. Computer Vision and Robotics Laboratory Computer Science Department Universidade Federal de Minas Gerais Belo HorizonteMG Brazil

In this paper we deal with the DTSP, which is the optimization problem where a path that goes through a set of two-dimensional points must be calculated considering the use of robots modeled as Dubins vehicles. Assuming that the sequence of visits is initially obtained accordingly to the ETSP, we propose an heuristic to assign orientations for each point in order to achieve a path which is length minimized and respects the vehicle’s nonholonomic constraints. The heuristic takes into account the vehicle’s minimum turning radius and distance between neighbors points to proportionally adjust the orientation on each point, allowing the definition of shorter Dubins curves connecting them. The methodology was horoughly evaluated through numerous trials in different simulated scenarios, providing statistical examination of the final results. © Springer International Publishing Switzerland 2014.

关键词： Vehicles

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Activation Template Matching Loss for Explainable Face Recognition 17

Activation Template Matching Loss for Explainable Face Recog...

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17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Lin, Huawei Liu, Haozhe Li, Qiufu Shen, Linlin Computer Vision Institute School of Computer Science and Softwre Enginnering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (纸本)9798350345445

Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets? In this paper, we propose a generic Explainable Channel Loss (ECLoss) to construct an explainable face recognition network. The explainable network trained with ECLoss can easily learn the facial part-based representation on the target convolutional layer, where an individual channel can detect a certain face part. Our experiments on dozens of datasets show that ECLoss achieves superior explainability metrics, and at the same time improves the performance of face verification without face alignment. In addition, our visualization results also illustrate the effectiveness of the proposed ECLoss. © 2023 IEEE.

关键词： Face recognition

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Sharing landmark information using mixture of Gaussian terrain spatiograms

Sharing landmark information using mixture of Gaussian terra...

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2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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

In this paper we evaluate the use of a novel spatial histogram, the terrain spatiogram, as a common representation for exchanging landmark information between robots working as a team to map an area. Individual robots use range sensors to provide the spatial dimension of the spatiogram and video for the image dimension. We have previously shown that terrain spatiograms can be shared between robots in a heterogeneous team to recognize landmarks and to fuse observations from multiple sensors or multiple platforms. A terrain spatiogram using a mixture of Gaussians (MOG) model is introduced and a corresponding normalized spatiogram similarity measure defined. Two methods to generate a MOG terrain spatiogram are presented and compared experimentally using indoor and outdoor landmark information transferred between two different models of robot equipped with differently configured stereocameras.

关键词： Robot kinematics Robot sensing systems Histograms Cognitive robotics Intelligent robots Image sensors Fuses Sensor fusion Navigation Hazards

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Learning to infer the depth map of a hand from its color image

arXiv

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arXiv 2018年

作者： Nicodemou, Vassilis C. Oikonomidis, Iason Tzimiropoulos, Georgios Argyros, Antonis Computational Vision and Robotics Laboratory Institute of Computer Science FORTH Greece Computer Science Department University of Crete Greece Computer Vision Laboratory University of Nottingham United Kingdom

We propose the first approach to the problem of inferring the depth map of a human hand based on a single RGB image. We achieve this with a Convolutional Neural Network (CNN) that employs a stacked hourglass model as its main building block. Intermediate supervision is used in several outputs of the proposed architecture in a staged approach. To aid the process of training and inference, hand segmentation masks are also estimated in such an intermediate supervision step, and used to guide the subsequent depth estimation process. In order to train and evaluate the proposed method we compile and make publicly available HandRGBD, a new dataset of 20,601 views of hands, each consisting of an RGB image and an aligned depth map. Based on HandRGBD, we explore variants of the proposed approach in an ablative study and determine the best performing one. The results of an extensive experimental evaluation demonstrate that hand depth estimation from a single RGB frame can be achieved with an accuracy of 22mm, which is comparable to the accuracy achieved by contemporary low-cost depth cameras. Such a 3D reconstruction of hands based on RGB information is valuable as a final result on its own right, but also as an input to several other hand analysis and perception algorithms that require depth input. Essentially, in such a context, the proposed approach bridges the gap between RGB and RGBD, by making all existing RGBD-based methods applicable to RGB input. Copyright © 2018, The Authors. All rights reserved.

关键词： Bridge approaches

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