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

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Traffic Light Detection: A Learning Algorithm and Evaluations on Challenging Dataset

Traffic Light Detection: A Learning Algorithm and Evaluation...

引用

International Conference on Intelligent Transportation

作者： Mark Philip Philipsen Morten Bornø Jensen Andreas Møgelmose Thomas B. Moeslund Mohan M. Trivedi UC San Diego Computer Vision and Robotics Research Laboratory La Jolla CA USA Visual Analysis of People Laboratory Aalborg University Aalborg Denmark

Traffic light recognition (TLR) is an integral part of any intelligent vehicle, which must function in the existing infrastructure. Pedestrian and sign detection have recently seen great improvements due to the introduction of learning based detectors using integral channel features. A similar push have not yet been seen for the detection sub-problem of TLR, where detection is dominated by methods based on heuristic models. Evaluation of existing systems is currently limited primarily to small local datasets. In order to provide a common basis for comparing future TLR research an extensive public database is collected based on footage from US roads. The database consists of both test and training data, totaling 46,418 frames and 112,971 annotated traffic lights, captured in continuous sequences under a varying light and weather conditions. The learning based detector achieves an AUC of 0.4 and 0.32 for day sequence 1 and 2, respectively, which is more than an order of magnitude better than the two heuristic model-based detectors.

关键词： Detectors Image color analysis Feature extraction Databases computer vision Intelligent vehicles Roads

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Cost-aware depth map estimation for Lytro camera

Cost-aware depth map estimation for Lytro camera

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作者： Kim, Min-Jung Oh, Tae-Hyun Kweon, In So Robotics and Computer Vision Laboratory KAIST Korea Republic of

ISBN: (纸本)9781479957514

Since commercial light field cameras became available, the light field camera has aroused much interest from computer vision and image processing communities due to its versatile functions. Most of its special features are based on an estimated depth map, so reliable depth estimation is a crucial step. However, estimating depth on real light field cameras is a challenging problem due to noise and short baselines among sub-aperture images. We propose a depth map estimation method for light field cameras by exploiting correspondence and focus cues. We aggregate costs among all the sub-aperture images on cost volume to alleviate noise effects. With efficiency of the cost volume, cost-aware depth estimation is quickly achieved by discrete-continuous optimization. In addition, we analyze each property of correspondence and focus cues and utilize them to select reliable anchor points. A well reconstructed initial depth map from the anchors is shown to enhance convergence. We show our method outperforms the state-of-the-art methods by validating it on real datasets acquired with a Lytro camera. © 2014 IEEE.

关键词： Cameras

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Day and night-time drive analysis using stereo vision for naturalistic driving studies

Day and night-time drive analysis using stereo vision for na...

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IEEE Symposium on Intelligent Vehicle

作者： Mark P. Philipsen Morten B. Jensen Ravi K. Satzoda Mohan M. Trivedi Andreas Møgelmose Thomas B. Moeslund Computer Vision and Robotics Research Laboratory La Jolla CA USA Visual Analysis of People Laboratory Aalborg University Aal-borg Denmark Aalborg Universitet Aalborg DK

In order to understand dangerous situations in the driving environment, naturalistic driving studies (NDS) are conducted by collecting and analyzing data from sensors looking inside and outside of the car. Manually processing the overwhelming amounts of data that are generated in such studies is very comprehensive. We propose a method for automatic data reduction for NDS based on stereo vision vehicle detection and tracking during day- and nighttime. The developed system can automatically register five NDS events, mainly related to intersections, from an existing NDS dictionary. We propose a new drive event which takes advantage of the extra dimension provided by stereo vision. In total, six drive events are selected on the basis of them being problematic to detect automatically using conventional monocular computer vision approaches. The proposed system is evaluated on day-and nighttime data, resulting in drive analysis report. The proposed system reach an overall precision of 0.78 and an overall recall of 0.72.

关键词： Vehicles Stereo vision Roads Three-dimensional displays Noise Vehicle detection Vehicle crash testing

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Balanced optical flow refinement by bidirectional constraint

Balanced optical flow refinement by bidirectional constraint

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作者： Choi, Jongwon Kim, Hyeongwoo Oh, Tae-Hyun Kweon, In So Robotics and Computer Vision Laboratory Korea Advanced Institute of Science and Technology Korea Republic of

ISBN: (纸本)9781479957514

We present an efficient optical flow refinement approach based on a bidirectional flow consistency. Our method is an add-on component that improves the existing optical flow estimation to be balanced between forward and backward flows. Most of the state-of-the-art optical flow methods only consider unidirectional motion vectors from a source image to a target image, which can make the estimated flow inconsistent with its backward estimation. The inconsistency can be reduced by considering the bidirectional motion when the optical flow is estimated, but it would be very hard for most of the typical optical flow methods and impossible for some of them. To solve this problem, we propose a sampling-based optimization method for efficiently refining the optical flows with a bidirectional constraint. By evaluating on Middle-bury benchmark and public large displacement datasets, we validate the effectiveness of our method quantitatively and qualitatively and for accuracy. © 2014 IEEE.

关键词： Optical flows

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Head pose tracking for immersive applications

Head pose tracking for immersive applications

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作者： Henriquez, Pedro Higuera, Oscar Matuszewski, Bogdan J. Robotics and Computer Vision Research Laboratory School of Computing Engineering and Physical Sciences University of Central Lancashire United Kingdom

ISBN: (纸本)9781479957514

The paper describes a 3D head pose tracking system designed for immersive applications. The proposed system is based on a random forest's head detection and pose regression model. The main novel contributions include the application, in immersive technologies, of a head tracker using a range data sensor, the Kalman filter and the random forest, with automatic detection of outliers and handling of the missing data. The envisaged applications of the proposed system include data normalization and data acquisition from remote locations in real-time. The latter is described in detail by integrating 3 degrees of freedom robotic camera head with the proposed head tracker. In this instance the proposed non-contact head tracking system is used to control the robotic camera by replicating measured operator's head motion to improve his/her spatial awareness and sense of immersion, with the captured video shown on a head mounted display. The experimental section includes accuracy and robustness analysis. The system's robustness is examined with respect to the head pose outliers and the missing head detections. The effects of the presence of the head mounted displays on the performance of the system is also assessed. Additionally a brief discussion of operators' psychophysical perception test is also included. © 2014 IEEE.

关键词： Kalman filters

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Cost-aware depth map estimation for Lytro camera

Cost-aware depth map estimation for Lytro camera

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IEEE International Conference on Image Processing

作者： Min-Jung Kim Tae-Hyun Oh In So Kweon Robotics and Computer Vision Laboratory KAIST

ISBN: (纸本)9781479957521

关键词： Cameras Estimation Reliability Optimization Three-dimensional displays computer vision Conferences

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Robotic Software System for the Disaster Circumstances: System of Team KAIST in the DARPA robotics Challenge Finals

Robotic Software System for the Disaster Circumstances: Syst...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Jeongsoo Lim Inwook Shim Okkee Sim Hyunmin Joe Inhyeok Kim Jungho Lee Jun-Ho Oh Humanoid Research Center School of Mechanical Aerospace & Systems Engineering Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Robotics and Computer Vision Laboratory Department of Electrical Engineering College of Information Science and Technology Korea Advanced Institute of Science and Technology Rainbow Co.

ISBN: (纸本)9781479968862

We developed a software system for operating the humanoid robot DRC-HUBO+ in disaster circumstances that the US Defense Advanced Research Projects Agency suggested. This system was developed under the consideration of the stability of whole system, the systemic software environment for multiple developers, and the recovery routine when the system encounters a seriously abnormal situation. With these design goals, we devised our strategy for three domains: system, vision, and communication. Following the strategy we made two software frameworks (PODO and VPC) and one logical flow of data for remote process management. With this software system, we conducted all Tasks in the DARPA robotics Challenge Finals, and we won the competition.

关键词： Software systems DARPA design target robotics Disasters Religious Missions flow datas

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Evolutionary Quasi-random Search for Hand Articulations Tracking

Evolutionary Quasi-random Search for Hand Articulations Trac...

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IEEE Conference on computer vision and Pattern Recognition

作者： Iason Oikonomidis Manolis I. A. Lourakis Antonis A. Argyros Computational Vision and Robotics Laboratory Institute of Computer Science

ISBN: (纸本)9781479951192

We present a new method for tracking the 3D position, global orientation and full articulation of human hands. Following recent advances in model-based, hypothesize-and-test methods, the high-dimensional parameter space of hand configurations is explored with a novel evolutionary optimization technique specifically tailored to the problem. The proposed method capitalizes on the fact that samples from quasi-random sequences such as the Sobol have low discrepancy and exhibit a more uniform coverage of the sampled space compared to random samples obtained from the uniform distribution. The method has been tested for the problems of tracking the articulation of a single hand (27D parameter space) and two hands (54D space). Extensive experiments have been carried out with synthetic and real data, in comparison with state of the art methods. The quantitative evaluation shows that for cases of limited computational resources, the new approach achieves a speed-up of four (single hand tracking) and eight (two hands tracking) without compromising tracking accuracy. Interestingly, the proposed method is preferable compared to the state of the art either in the case of limited computational resources or in the case of more complex (i.e., higher dimensional) problems, thus improving the applicability of the method in a number of application domains.

关键词： Computational modeling Optimization Kinematics Tracking Joints Accuracy Linear programming

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BALANCED OPTICAL FLOW REFINEMENT BY BIDIRECTIONAL CONSTRAINT

BALANCED OPTICAL FLOW REFINEMENT BY BIDIRECTIONAL CONSTRAINT

引用

IEEE International Conference on Image Processing

作者： Jongwon Choi Hyeongwoo Kim Tae-Hyun Oh In So Kweon Robotics and Computer Vision Laboratory Korea Advanced Institute of Science and Technology

ISBN: (纸本)9781479957521

We present an efficient optical flow refinement approach based on a bidirectional flow consistency. Our method is an add-on component that improves the existing optical flow estimation to be balanced between forward and backward flows. Most of the state-of-the-art optical flow methods only consider unidirectional motion vectors from a source image to a target image, which can make the estimated flow inconsistent with its backward estimation. The inconsistency can be reduced by considering the bidirectional motion when the optical flow is estimated, but it would be very hard for most of the typical optical flow methods and impossible for some of them. To solve this problem, we propose a sampling-based optimization method for efficiently refining the optical flows with a bidirectional constraint. By evaluating on Middlebury benchmark and public large displacement datasets, we validate the effectiveness of our method quantitatively and qualitatively and for accuracy.

关键词： Optical flow Bidirectional constraint Particle filter-based optimization

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vision on Wheels: Looking at Driver, Vehicle, and Surround for On-Road Maneuver Analysis

Vision on Wheels: Looking at Driver, Vehicle, and Surround f...

引用

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

作者： Eshed Ohn-Bar Ashish Tawari Sujitha Martin Mohan M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego

Automotive systems provide a unique opportunity for mobile vision technologies to improve road safety by understanding and monitoring the driver. In this work, we propose a real-time framework for early detection of driver maneuvers. The implications of this study would allow for better behavior prediction, and therefore the development of more efficient advanced driver assistance and warning systems. Cues are extracted from an array of sensors observing the driver (head, hand, and foot), the environment (lane and surrounding vehicles), and the ego-vehicle state (speed, steering angle, etc.). Evaluation is performed on a real-world dataset with overtaking maneuvers, showing promising results. In order to gain better insight into the processes that characterize driver behavior, temporally discriminative cues are studied and visualized.

关键词： Vehicles Sensors Cameras Foot Histograms Radar tracking Vehicle dynamics

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