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检索条件"任意字段=27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014"

共 227 条记录，以下是171-180 订阅

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Exploiting Traffic Scene Disparity Statistics for Stereo vision

Exploiting Traffic Scene Disparity Statistics for Stereo Vis...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Gehrig, Stefan K. Franke, Uwe Schneider, Nicolai Daimler AG HPC 050-G024 D-71059 Sindelfingen Germany IT Designers GmbH D-73070 Esslingen Germany

ISBN: (纸本)9781479943098

Advanced Driver Assistance Systems benefit from a full 3D reconstruction of the environment in real-time, often obtained via stereo vision. Semi-Global Matching (SGM) is a popular stereo algorithm for solving this task which is already in use for production vehicles. Despite this progess, one key challenge remains: stereo vision during adverse weather conditions such as rain, snow and low-lighting. Current methods generate many disparity outliers and false positives on a segmentation level under such conditions. these shortcomings are alleviated by integrating prior scene knowledge. We formulate a scene prior that exploits knowledge of a representative traffic scene, which we apply to SGM and Graph Cut based disparity estimation. the prior is learned from traffic scene statistics extracted during good weather. Using this prior, the object detection rate is maintained on a driver assistance database of 3000 frames including bad weather while reducing the false positive rate significantly. Similar results are obtained for the KITTI dataset, maintaining excellent performance in good weather conditions. We also show that this scene prior is easy and efficient to implement both on CPU platforms and on reconfigurable hardware platforms. the concept can be extended to other application areas such as indoor robotics, when prior information of the disparity distribution is gathered.

关键词： FPGA embedded vision scene prior semi-global matching stereo vision

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Globality-Locality Preserving Projections for Biometric Data Dimensionality Reduction

Globality-Locality Preserving Projections for Biometric Data...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Huang, Sheng Elgammal, Ahmed Huangfu, Luwen Yang, Dan Zhang, Xiaohong Chongqing Univ Chongqing Peoples R China HTC Beijing Adv Technol Res Ctr Beijing Peoples R China Rutgers State Univ Piscataway NJ 08855 USA

ISBN: (纸本)9781479943098

In a biometric recognition task, the manifold of data is the result of the interactions between the sub-manifold of dynamic factors of subjects and the sub-manifold of static factors of subjects. therefore, instead of directly constructing the graph Laplacian of samples, we firstly divide each subject data into a static part (subject-invariant part) and a dynamic part (intra-subject variations) and then jointly learn their graph Laplacians to yield a new graph Laplcian. We use this new graph Laplacian to replace the original graph Laplacian of Locality Preserving Projections (LPP) to present a new supervised dimensionality reduction algorithm. We name this algorithm Globality-Locality Preserving Projections (GLPP). Moreover, we also extend GLPP into a 2D version for dimensionality reduction of 2D data. Compared to LPP, the subspace learned by GLPP more precisely preserves the manifold structures of the data and is more robust to the noisy samples. We apply it to face recognition and gait recognition. Extensive results demonstrate the superiority of GLPP in comparison with the state-of-the-art algorithms.

关键词： Dimensionality Reduction Face recognition Gait recognition Graph Laplacian Subspace Learning

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A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks

A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Gokhale, Vinayak Jin, Jonghoon Dundar, Aysegul Martini, Berin Culurciello, Eugenio Purdue Univ W Lafayette IN 47907 USA Purdue Univ Weldon Sch Biomed Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781479943098

Deep networks are state-of-the-art models used for understanding the content of images, videos, audio and raw input data. Current computing systems are not able to run deep network models in real-time with low power consumption. In this paper we present nn-X: a scalable, low-power coprocessor for enabling real-time execution of deep neural networks. nn-X is implemented on programmable logic devices and comprises an array of configurable processing elements called collections. these collections perform the most common operations in deep networks: convolution, subsampling and non-linear functions. the nn-X system includes 4 high-speed direct memory access interfaces to DDR3 memory and two ARM Cortex-A9 processors. Each port is capable of a sustained throughput of 950 MB/s in full duplex. nn-X is able to achieve a peak performance of 227 G-ops/s, a measured performance in deep learning applications of up to 200 G-ops/s while consuming less than 4 watts of power. this translates to a performance per power improvement of 10 to 100 times that of conventional mobile and desktop processors.

关键词： computer vision convolutional neural networks embedded vision system hardware acceleration machine learning

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A Novel HDR Depth Camera for Real-time 3D 360° Panoramic vision

A Novel HDR Depth Camera for Real-time 3D 360° Panoramic Vi...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Belbachir, Ahmed Nabil Schraml, Stephan Mayerhofer, Manfred Hofstaetter, Michael AIT Austrian Inst Technol New Sensor Technol Business Unit Safety & Secur Dept A-1220 Vienna Austria

ISBN: (纸本)9781479943098

this paper presents a novel 360 degrees High-Dynamic Range (HDR) camera for real-time 3D 360 degrees panoramic computer vision. the camera consists of (1) a pair of bio-inspired dynamic vision line sensors (1024 pixel each) asynchronously generating events at high temporal resolution with on-chip time stamping (1 mu s resolution), having a high dynamic range and the sparse visual coding of the information, (2) a high-speed mechanical device rotating at up to 10 revolutions per sec (rps) on which the pair of sensor is mounted and (3) a processing unit for the configuration of the detector chip and transmission of its data through a slip ring and a gigabit Ethernet communication to the user. Within this work, we first present the new camera, its individual components and resulting panoramic edge map. In a second step, we developed a method for reconstructing the intensity images out of the event data generated by the sensors. the algorithm maps the recorded panoramic views into graylevel images by using a transform coefficient. In the last part of this work, anaglyph representation and 3D reconstruction results out of the stereo images are shown. the experimental results show the capabilities of the new camera to generate 10 x 3D panoramic views per second in real-time at an image resolution of 5000x1024 pixel and intra-scene dynamic range of more than 120 dB under natural illuminations. the camera potential for 360 degrees depth imaging and mobile computer vision is briefly highlighted.

关键词： 3D 360° panoramic vision dynamic vision sensors event-driven vision HDR imaging

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Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph 27

Multiple Target Tracking Based on Undirected Hierarchical Re...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wen, Longyin Li, Wenbo Yan, Junjie Lei, Zhen Yi, Dong Li, Stan Z. Chinese Acad Sci Ctr Biometr & Secur Res Beijing 100864 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

Multi-target tracking is an interesting but challenging task in computer vision field. Most previous data association based methods merely consider the relationships (e.g. appearance and motion pattern similarities) between detections in local limited temporal domain, leading to their difficulties in handling long-term occlusion and distinguishing the spatially close targets with similar appearance in crowded scenes. In this paper, a novel data association approach based on undirected hierarchical relation hyper-graph is proposed, which formulates the tracking task as a hierarchical dense neighborhoods searching problem on the dynamically constructed undirected affinity graph. the relationships between different detections across the spatio-temporal domain are considered in a high-order way, which makes the tracker robust to the spatially close targets with similar appearance. Meanwhile, the hierarchical design of the optimization process fuels our tracker to long-term occlusion with more robustness. Extensive experiments on various challenging datasets (i.e. PETS2009 dataset, ParkingLot), including both low and high density sequences, demonstrate that the proposed method performs favorably against the state-of-the-art methods.

关键词： Multi-target tracking dense neighborhoods searching hypergraph undirected affinity graph

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Multi-Source Multi-Modal Activity recognition in Aerial Video Surveillance

Multi-Source Multi-Modal Activity Recognition in Aerial Vide...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hammoud, Riad I. Sahin, Cem S. Blasch, Erik P. Rhodes, Bradley J. BAE Syst Burlington MA 01803 USA Air Force Res Lab Rome NY USA

ISBN: (纸本)9781479943098

Recognizing activities in wide aerial/overhead imagery remains a challenging problem due in part to low-resolution video and cluttered scenes with a large number of moving objects. In the context of this research, we deal with two unsynchronized data sources collected in real-world operating scenarios: full-motion videos (FMV) and analyst call-outs (ACO) in the form of chat messages (voice-to-text) made by a human watching the streamed FMV from an aerial platform. We present a multi-source multi-modal activity/event recognition system for surveillance applications, consisting of: (1) detecting and tracking multiple dynamic targets from a moving platform, (2) representing FMV target tracks and chat messages as graphs of attributes, (3) associating FMV tracks and chat messages using a probabilistic graph-based matching approach, and (4) detecting spatial-temporal activity boundaries. We also present an activity pattern learning framework which uses the multi-source associated data as training to index a large archive of FMV videos. Finally, we describe a multi-intelligence user interface for querying an index of activities of interest (AOIs) by movement type and geo-location, and for playing-back a summary of associated text (ACO) and activity video segments of targets-of-interest (TOIs) (in both pixel and geo-coordinates). Such tools help the end-user to quickly search, browse, and prepare mission reports from multi-source data.

关键词： FMV exploitation MINER activity recognition chat and video fusion event recognition fusion graph matching graph representation surveillance

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Coarse-to-Fine Segmentation With Shape-Tailored Continuum Scale Spaces 30

Coarse-to-Fine Segmentation With Shape-Tailored Continuum Sc...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Khan, Naeemullah Hong, Byung-Woo Yezzi, Anthony Sundaramoorthi, Ganesh KAUST Thuwal Saudi Arabia Chung Ang Univ Seoul South Korea Georgia Tech Atlanta GA USA

ISBN: (纸本)9781538604571

We formulate an energy for segmentation that is designed to have preference for segmenting the coarse over fine structure of the image, without smoothing across boundaries of regions. the energy is formulated by integrating a continuum of scales from a scale space computed from the heat equation within regions. We show that the energy can be optimized without computing a continuum of scales, but instead from a single scale. this makes the method computationally efficient in comparison to energies using a discrete set of scales. We apply our method to texture and motion segmentation. Experiments on benchmark datasets show that a continuum of scales leads to better segmentation accuracy over discrete scales and other competing methods.

关键词： Image segmentation Space heating computer vision Motion segmentation Mathematical model Smoothing methods Motion segmentation smoothing methods Space heating images computer vision image segmentation Mathematical Model Continuum segmentation Dermal denticles Energy sources BALANCES IMAGE STRUCTURE Scale Segmentation

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Driver Cell Phone Usage Detection From HOV/HOT NIR Images

Driver Cell Phone Usage Detection From HOV/HOT NIR Images

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Artan, Yusuf Bulan, Orhan Loce, Robert P. Paul, Peter Xerox Res Ctr Webster Webster NY 14580 USA

ISBN: (纸本)9781479943098

Distracted driving due to cell phone usage is an increasingly costly problem in terms of lost lives and damaged property. Motivated by its impact on public safety and property, several state and federal governments have enacted regulations that prohibit driver mobile phone usage while driving. these regulations have created a need for cell phone usage detection for law enforcement. In this paper, we propose a computer vision based method for determining driver cell phone usage using a near infrared (NIR) camera system directed at the vehicle's front windshield. the developed method consists of two stages;first, we localize the driver's face region within the front windshield image using the deformable part model (DPM). Next, we utilize a local aggregation based image classification technique to classify a region of interest (ROI) around the drivers face to detect the cell phone usage. We propose two classification architectures by using full face and half face images for classification and compare their performance in terms of accuracy, specificity, and sensitivity. We also present a comparison of various local aggregation-based image classification methods using bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD) and Fisher vectors (FV). A data set of 1500 images was collected on a public roadway and is used to perform the experiments.

关键词： cell phone usage detection image classification Image classification cell phone use Drivers images driver circuits full face computer vision Windscreen Public safety Law Enforcement

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Expanded Parts Model for Human Attribute and Action recognition in Still Images

Expanded Parts Model for Human Attribute and Action Recognit...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Sharma, Gaurav Jurie, Frederic Schmid, Cordelia Univ Caen Basse Normandie CNRS GREYC UMR 6072 Caen France INRIA LEAR Grenoble France

ISBN: (纸本)9780769549897

We propose a new model for recognizing human attributes (e.g. wearing a suit, sitting, short hair) and actions (e.g. running, riding a horse) in still images. the proposed model relies on a collection of part templates which are learnt discriminatively to explain specific scale-space locations in the images (in human centric coordinates). It avoids the limitations of highly structured models, which consist of a few (i.e. a mixture of) 'average' templates. To learn our model, we propose an algorithm which automatically mines out parts and learns corresponding discriminative templates with their respective locations from a large number of candidate parts. We validate the method on recent challenging datasets: (i) Willow 7 actions [7], (ii) 27 Human Attributes (HAT) [25], and (iii) Stanford 40 actions [37]. We obtain convincing qualitative and state-of-the-art quantitative results on the three datasets.

关键词： image recognition

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Detecting Social Groups in Crowded Surveillance Videos Using Visual Attention

Detecting Social Groups in Crowded Surveillance Videos Using...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Leach, Michael. J. V. Baxter, Rolf. Robertson, Neil. M. Sparks, Ed. P. Roke Manor Res Romsey Hants England Heriot Watt Univ Sch Engn & Phys Sci Edinburgh Midlothian Scotland

ISBN: (纸本)9781479943098

In this paper we demonstrate that the current state of the art social grouping methodology can be enhanced with the use of visual attention estimation. In a surveillance environment it is possible to extract the gazing direction of pedestrians, a feature which can be used to improve social grouping estimation. We implement a state of the art motion based social grouping technique to get a baseline success at social grouping, and implement the same grouping with the addition of the visual attention feature. By a comparison of the success at finding social groups for two techniques we evaluate the effectiveness of including the visual attention feature. We test both methods on two datasets containing busy surveillance scenes. We find that the inclusion of visual interest improves the motion social grouping capability. For the Oxford data, we see a 5.6% improvement in true positives and 28.5% reduction in false positives. We see up to a 50% reduction in false positives in other datasets. the strength of the visual feature is demonstrated by the association of social connections that are otherwise missed by the motion only social grouping technique.

关键词： Video surveillance computer aided analysis Machine vision

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