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A GPU-based implementation of motion detection from a moving platform

A GPU-based implementation of motion detection from a moving...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Qian Yu Gerard Medioni Institute for Robotics and Intelligent Systems University of Southern California USA

We describe a GPU-based implementation of motion detection from a moving platform. Motion detection from a moving platform is inherently difficult as the moving camera induces 2D motion field in the entire image. A step compensating for camera motion is required prior to estimating of the background model. Due to inevitable registration errors, the background model is estimated according to a sliding window of frames to avoid the case where erroneous registration influences the quality of the detection for the whole sequence. However, this approach involves several characteristics that put a heavy burden on real-time CPU implementation. We exploit GPU to achieve significant acceleration over standard CPU implementations. Our GPU-based implementation can build the background model and detect motion regions at around 18 fps on 320times240 videos that are captured for a moving camera.

关键词： Motion detection Cameras Videos Intelligent robots Robot vision systems Motion estimation Image sequences Statistics Pixel Intelligent systems

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Particle filtering with rendered models: A two pass approach to multi-object 3D tracking with the GPU

Particle filtering with rendered models: A two pass approach...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Erik Murphy-Chutorian Mohan M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego USA

We describe a new approach to vision-based 3D object tracking, using appearance-based particle filters to follow 3D model reconstructions. This method is targeted towards modern graphics processors, which are optimized for 3D reconstruction and are capable of highly parallel computation. We discuss an OpenGL implementation of this approach, which uses two rendering passes to update the particle filter weights. In the first pass, the system renders the previous object state estimates to an off-screen framebuffer. In the second pass, the system uses a programmable vertex shader to compute the mean normalized cross-correlation between each sample and the subsequent video frame. The particle filters are updated using the correlation scores and provide a full 3D track of the objects. We provide examples for tracking human heads in both single and multi-camera scenarios.

关键词： Filtering Particle tracking Rendering (computer graphics) Particle filters Target tracking Graphics Optimization methods Concurrent computing State estimation Humans

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Image authentication by detecting traces of demosaicing

Image authentication by detecting traces of demosaicing

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Andrew C. Gallagher Tsuhan Chen Carnegie Mellon University USA Eastman Kodak (Japan) Limited USA

With increasing technical advances, computer graphics are becoming more photorealistic. Therefore, it is important to develop methods for distinguishing between actual photographs from digital cameras and computer generated images. We describe a novel approach to this problem. Rather than focusing on the statistical differences between the image textures, we recognize that images from digital cameras contain traces of resampling as a result of using a color filter array with demosaicing algorithms. We recognize that estimation of the actual demosaicing parameters is not necessary; rather, detection of the presence of demosaicing is the key. The in-camera processing (rather than the image content) distinguishes the digital camera photographs from computer graphics. Our results show high reliability on a standard test set of JPEG compressed images from consumer digital cameras. Further, we show the application of these ideas for accurately localizing forged regions within digital camera images.

关键词： Authentication Digital cameras computer graphics Image generation Image texture Image recognition Digital filters Testing Transform coding Image coding

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Visual map matching and localization using a global feature map

Visual map matching and localization using a global feature ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Oliver Pink Institut fur Mess und Regelungstechnik Universitat Karlsruhe Karlsruhe Germany

This paper presents a novel method to support environmental perception of mobile robots by the use of a global feature map. While typical approaches to simultaneous localization and mapping (SLAM) mainly rely on an on-board camera for mapping, our approach uses geographically referenced aerial or satellite images to build a map in advance. The current position on the map is determined by matching features from the on-board camera to the global feature map. The problem of feature matching is posed as a standard point pattern matching problem and a solution using the iterative closest point method is given. The proposed algorithm is designed for use in a street vehicle and uses lane markings as features, but can be adapted to almost any other type of feature that is visible in aerial images. Our approach allows for estimating the robot position at a higher precision than by a purely GPS-based localization, while at the same time providing information about the environment far beyond the current field of view.

关键词： Simultaneous localization and mapping Cameras pattern matching Mobile robots Robot vision systems Satellites Iterative methods Iterative algorithms Algorithm design and analysis Vehicles

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Efficient scan-window based object detection using GPGPU

Efficient scan-window based object detection using GPGPU

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Li Zhang Ramakant Nevatia Institute of Robotics and Intelligent Systems University of Southern California USA

We describe an efficient design for scan-window based object detectors using a general purpose graphics hardware computing (GPGPU) framework. While the design is particularly applied to built a pedestrian detector that uses histogram of oriented gradient (HOG) features and the support vector machine (SVM) classifiers, the methodology we use is generic and can be applied to other objects, using different features and classifiers. The GPGPU paradigm is utilized for feature extraction and classification, so that the scan windows can be processed in parallel. We further propose to precompute and cache all the histograms in advance, instead of using integral images, which greatly lowers the computation cost. A multi-scale reduce strategy is employed to save expensive CPU-GPU data transfers. Experimental results show that our implementation achieves a more-than-ten-times speed up with no loss on detection rates.

关键词： Object detection Detectors Graphics Hardware Support vector machines Support vector machine classification Rendering (computer graphics) Histograms Feature extraction Intelligent robots

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Real-time estimation of human attention field in LWIR and color surveillance videos

Real-time estimation of human attention field in LWIR and co...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Alex Leykin Riad Hammoud Department of Computer Science Indiana University Bloomington IN USA Delphi Electronics and Safety Delphi Communications Systems Inc. IN USA

Knowing the visual attention field of a monitored subject is of great value for many applications including surveillance and marketing. This paper proposes first to track peoplepsilas bodies, and then estimates visual attention field for each human using head pose information. The proposed head pose technique aims at estimating the yaw angle only. The method is shown to operate on monocular color camera sequences and is further refined with the data from a thermal sensor. In typical monocular tracking sequences the resolution of the head is very low and parts of the head are occluded with the face often invisible to the camera. We propose a method of combining a skin color detector with the direction of motion in a probabilistic way. We show how head profile obtained from the thermal sequence can be used to further improve the result.

关键词： Humans Surveillance Videos Cameras Monitoring Thermal sensors Face detection Skin Motion detection Detectors

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Sticky vector fields, and other geometric measures on diffusion tensor images

Sticky vector fields, and other geometric measures on diffus...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Laura Astola Luc Florack Department of Mathematics and Computer Science Eindhovan University of Technology Eindhoven Netherlands

This paper is about geometric measures in diffusion tensor imaging (DTI) analysis, and it is a continuation of our previous work (L. Astola et al., 2007), where we discussed two measures for diffusion tensor (DT) image (fiber tractography) analysis. Its contribution is threefold. First, we show how the so called connectivity measure performs on a real DTI image with three different interpolation methods. Secondly, we introduce a new vector field on DTI images, that points out the locally most coherent direction for fiber tracking, and we illustrate it on bundles of tracked fibers. Thirdly, we introduce an inhomogeneity- (edge-, crossing-) detector for symmetric positive matrix valued images, including DTI images. One possible application is segmentation of diffusion tensor fields.

关键词： Tensile stress Diffusion tensor imaging Optical fiber testing Image analysis Interpolation Image edge detection Image segmentation Karhunen-Loeve transforms Anisotropic magnetoresistance Mathematics

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Extending two non-parametric transforms for FPGA based stereo matching using bayer filtered cameras

Extending two non-parametric transforms for FPGA based stere...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Kristian Ambrosch Martin Humenberger Wilfried Kubinger Andreas Steininger Austrian Research Centers Gesellschaft mit beschränkter Haftung ARC Vienna Austria University of Technology Vienna Vienna Austria

Stereo vision has become a very interesting sensing technology for robotic platforms. It offers various advantages, but the drawback is a very high algorithmic effort. Due to the aptitude of certain non-parametric techniques for field programmable gate array (FPGA) based stereo matching, these algorithms can be implemented in highly parallel design while offering adequate real-time behavior. To enable the provision of color images by the stereo sensor for object classification tasks, we propose a technique for extending the rank and the census transform for increased robustness on gray scaled Bayer patterned images. Furthermore, we analyze the extended and the original algorithmspsila behavior on image sets created in controlled environments as well as on real world images and compare their resource usage when implemented on our FPGA based stereo matching architecture.

关键词： Field programmable gate arrays Matched filters Cameras Robot vision systems Algorithm design and analysis Stereo vision Robot sensing systems Color Image sensors Robustness

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3D Spatio-Temporal face recognition using dynamic range model sequences

3D Spatio-Temporal face recognition using dynamic range mode...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yi Sun Lijun Yin Department of Computer Science State University of New York Binghamton Binghamton NY USA

Research on 3D face recognition has been intensified in recent years. However, most research has focused on the 3D static data analysis. In this paper, we investigate the face recognition problem using dynamic 3D face model sequences. Based on our newly created 3D dynamic face database, we propose to use a spatio-temporal hidden Markov model (HMM) which incorporates 3D surface feature characterization to learn the spatial and temporal information of faces. The advantage of using 3D dynamic data for face recognition has been evaluated by comparing our approach to three conventional approaches: 2D video based temporal HMM model, conventional 2D-texture based approach (e.g., Gabor wavelet based approach), and static 3D-model-based approaches.

关键词： Face recognition Dynamic range Hidden Markov models Spatial databases Lighting Surface fitting Video sequences Sun computer science Data analysis

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New insights into the calibration of ToF-sensors

New insights into the calibration of ToF-sensors

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Marvin Lindner Andreas Kolb Thorsten Ringbeck Institute for Vision and Graphics University of Siegen Germany PMD Technologies Siegen Germany

Time-of-flight (ToF) sensors have become an alternative to conventional distance sensing techniques like laser scanners or image based stereo. ToF sensors provide full range distance information at high frame-rates and thus have a significant impact onto current research in areas like online object recognition, collision prevention or scene reconstruction. However, ToF cameras like the photonic mixer device (PMD) still exhibit a number of challenges regarding static and dynamic effects, e.g. systematic distance errors and motion artefacts, respectively. Sensor calibration techniques reducing static system errors have been proposed and show promising results. However, current calibration techniques in general need a large set of reference data in order to determine the corresponding parameters for the calibration model. This paper introduces a new calibration approach which combines different demodulation techniques for the ToF- camera 's reference signal. Examples show, that the resulting combined demodulation technique yields improved distance values based on only two required reference data sets.

关键词： Calibration

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