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Conference on computer vision and Pattern Recognition (CVPR)

作者： Li Zhang Bo Wu Ram Nevatia Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

Use of IR images is advantageous for many surveillance applications where the systems must operate around the clock and external illumination is not always available. We investigate the methods derived from visible spectrum analysis for the task of human detection. Two feature classes (edgelets and HOG features) and two classification models(AdaBoost and SVM cascade) are extended to IR images. We find out that it is possible to get detection performance in IR images that is comparable to state-of-the-art results for visible spectrum images. It is also shown that the two domains share many features, likely originating from the silhouettes, in spite of the starkly different appearances of the two modalities.

关键词： Infrared detectors Infrared imaging Shape Surveillance Clocks Lighting Humans computer vision Image edge detection Support vector machines

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Robust moving object detection at distance in the visible spectrum and beyond using a moving camera

Robust moving object detection at distance in the visible sp...

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2006 Conference on computer vision and Pattern Recognition workshops

作者： Zhang, Yan Kiselewich, Stephen J. Bauson, William A. Hammoud, Riad Delphi Electronics and Safety Kokomo IN 46904-9005

ISBN: (纸本)0769526462

Automatic detection of moving objects at distance and in all weather conditions is a critical task in many vision-based safety applications such as video surveillance and vehicle forewarn collision warning. In such applications, prior knowledge about the object class (vehicle, pedestrian, tree, etc.) and imaging conditions (shadow, depth) is unavailable. What makes the task even more challenging is when the camera is non-stationary, e.g., mounted on a moving vehicle. The essential problem in this case lies in distinguishing between camera-induced motion and independent motion. This paper proposes a robust algorithm for automatic moving object detection at distance. The camera is mounted on a moving vehicle and operates in both day and night time. Through the utilization of the focus of expansion (FOE) and its associated residual map, the proposed method is able to detect and separate independently moving objects (IMOs) from the "moving" background caused by the camera motion. Experimentations on numerous real-world driving videos have shown the effectiveness of the proposed technique. Moving objects such as pedestrians and vehicles up to 40 meters away from the camera have been reliably detected at 10 frames per second on a 1.5GHz PC. © 2006 ieee.

关键词： Object recognition

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Robust Moving Object Detection at Distance in the visible spectrum and beyond Using A Moving Camera

Robust Moving Object Detection at Distance in the Visible Sp...

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ieee computer Society Conference on computer vision and Pattern Recognition workshops (CVPRW)

作者： Yan Zhang S.J. Kiselewich W.A. Bauson R. Hammoud Delphi Electronics and Safety Kokomo IN USA

Automatic detection of moving objects at distance and in all weather conditions is a critical task in many visionbased safety applications such as video surveillance and vehicle forewarn collision warning. In such applications, prior knowledge about the object class (vehicle, pedestrian, tree, etc.) and imaging conditions (shadow, depth) is unavailable. What makes the task even more challenging is when the camera is non-stationary, e.g., mounted on a moving vehicle. The essential problem in this case lies in distinguishing between camera-induced motion and independent motion. This paper proposes a robust algorithm for automatic moving object detection at distance. The camera is mounted on a moving vehicle and operates in both day and night time. Through the utilization of the focus of expansion (FOE) and its associated residual map, the proposed method is able to detect and separate independently moving objects (IMOs) from the "moving" background caused by the camera motion. Experimentations on numerous realworld driving videos have shown the effectiveness of the proposed technique. Moving objects such as pedestrians and vehicles up to 40 meters away from the camera have been reliably detected at 10 frames per second on a 1.8GHz PC.

关键词： Robustness Object detection Cameras Vehicle safety Motion detection Motion estimation Vehicle detection Video surveillance Pattern recognition Subtraction techniques

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beyond RANSAC: User Independent Robust Regression

Beyond RANSAC: User Independent Robust Regression

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ieee computer Society Conference on computer vision and Pattern Recognition workshops (CVPRW)

作者： R. Subbarao P. Meer Department of Electrical and Computer Engineering Rutgers University Piscataway NJ USA

RANSAC is the most widely used robust regression algorithm in computer vision. However, RANSAC has a few drawbacks which make it difficult to use in a lot of applications. Some of these problems have been addressed through improved sampling algorithms or better cost functions, but an important problem still remains. The algorithms are not user independent, and require some knowledge of the scale of the inlier noise. The projection based M-estimator (pbM) offers a solution to this by reframing the regression problem in a projection pursuit framework. In this paper we derive the pbM algorithm for heteroscedastic data. Our algorithm is applied to various real problems and its performance is compared with RANSAC and MSAC. It is shown that pbM gives better results than RANSAC and MSAC in spite of being user independent.

关键词： computer vision Application software Sampling methods Motion estimation Parameter estimation Cost function Interference Noise robustness Project management Kernel

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Performance evaluation of face recognition using visual and thermal imagery with advanced correlation filters

Performance evaluation of face recognition using visual and ...

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2005 ieee computer Society Conference on computer vision and Pattern Recognition, CVPR 2005 - workshops

作者： Heo, Jingu Savvides, Marios Vijayakumar, B.V.K. Department of Electrical and Computer Engineering Carnegie Mellon University United States

ISBN: (纸本)0769526608

This paper presents the face recognition performance evaluation using visual and thermal infrared (IR) face images with correlation filter methods. New correlation filter designs have shown to be distortion invariant and the advantages of using thermal IR images are due to their invariance to visible illumination variations. A combined use of thermal IR image data and correlation filters makes a viable means for improving the performance of face recognition techniques, especially beyond visual spectrum. Subset of Equinox databases are used for the performance evaluation. Among various advanced correlation filters, minimum average correlation energy (MACE) filters and optimum trade-off synthetic discriminant function (OTSDF) filters are used in our experiments. We show that correlation filters perform well when the size of face is of significantly low resolution (e.g. 20x20 pixels). Performing robust face recognition using low resolution images has many applications including performing human identification at a distance (HID). The eyeglass detection and removal in thermal images are processed to increase the performance in thermal face recognition. We show that we can outperform commercial face recognition algorithms such as FaceIt® based on Local Feature Analysis (LFA). © 2005 ieee computer Society. All rights reserved.

关键词： Face recognition

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Performance Evaluation of Face Recognition using Visual and Thermal Imagery with Advanced Correlation Filters

Performance Evaluation of Face Recognition using Visual and ...

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ieee computer Society Conference on computer vision and Pattern Recognition workshops (CVPRW)

作者： Jingu Heo M. Savvides B.V.K. Vijayakumar Department of Electrical and Computer Engineering Carnegie Mellon University USA

关键词： Face recognition Filters Face detection Energy resolution Infrared imaging Lighting Image databases Visual databases Robustness Image resolution

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Proceedings ieee workshop on computer vision beyond the visible spectrum: methods and applications (Cat. ***00640)

Proceedings IEEE Workshop on Computer Vision Beyond the Visi...

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ieee workshop on computer vision beyond the visible spectrum: methods and applications

Presents the title page of the proceedings record.

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Face detection in the near-IR spectrum

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IMAGE AND vision COMPUTING 2003年第7期21卷 565-578页

作者： Dowdall, J Pavlidis, I Bebis, G Univ Houston Dept Comp Sci Visual Comp Lab Houston TX 77204 USA Univ Nevada Dept Comp Sci Comp Vis Lab Reno NV 89557 USA

Face detection is an important prerequisite step for successful face recognition. The performance of Previous face detection methods reported in the literature is far from perfect and deteriorates ungracefully where lighting conditions cannot be controlled. We propose a method that outperforms state-of-the-art face detection methods in environments with stable lighting. In addition, our method can potentially perform well in environments with variable lighting conditions. The approach capitalizes upon our near-IR skin detection method reported elsewhere [Proceedings ieee workshop on computer vision beyond the visible spectrum: methods and applications;2000, ieee Trans. Int. Trans. Sys.: vol. 1: 72-85]. It ascertains the existence of a face within the skin region by finding the eyes and eyebrows. The eye-eyebrow pairs are determined by extracting appropriate features from multiple near-IR bands. Very successful feature extraction is achieved by simple algorithmic means like integral projections and template matching. This is because processing is constrained in the skin region and aided by the near-IR phenomenology. The effectiveness of our method is substantiated by comparative experimental results with the Identix face detector [http://***]. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： < near-IR phenomenology face detection integral projection

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Face detection in the near-IR spectrum

Face detection in the near-IR spectrum

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ieee workshop in computer vision beyond the visible spectrum

作者： Dowdall, J Pavlidis, I Bebis, G Univ Houston Dept Comp Sci Visual Comp Lab Houston TX 77204 USA Univ Nevada Dept Comp Sci Comp Vis Lab Reno NV 89557 USA

关键词： < near-IR phenomenology face detection integral projection

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Proceedings - ieee workshop on computer vision beyond the visible spectrum: methods and applications, CVBVS 1999

Proceedings - IEEE Workshop on Computer Vision Beyond the Vi...

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1st ieee workshop on computer vision beyond the visible spectrum: methods and applications, CVBVS 1999

ISBN: (纸本)0769500501

The proceedings contain 14 papers. The topics discussed include: detecting moving objects in airborne forward looking infra-red sequences;moving target detection in infrared imagery using a regularized CDWT optical flow;dual-band ATR for forward-looking infrared images;extending color vision methods to bands beyond the visible;a near-infrared fusion scheme for automatic detection of vehicle passengers;invariants of the LWIR thermophysical model;passive night vision sensor comparison for unmanned ground vehicle stereo vision navigation;the dynamic database (DDB) program;adaptive target recognition;a ground truth tool for synthetic aperture radar (SAR) imagery;quasi-invariants for recognition of articulated and non-standard objects in SAR images;morphological shared-weight neural networks: a tool for automatic target recognition beyond the visible spectrum;a machine vision system using a laser radar applied to robotic fruit harvesting;LADAR scene description using fuzzy morphology and rules;and hyper-spectral image processing applications on the SIMD pixel processor for the digital battlefield.

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