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检索条件"任意字段=2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014"

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Improved Cooperative Stereo Matching for Dynamic vision Sensors with Ground Truth Evaluation 30

Improved Cooperative Stereo Matching for Dynamic Vision Sens...

引用

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

作者： Piatkowska, Ewa Kogler, Juergen Belbachir, Ahmed Nabil Gelautz, Margrit AIT Vienna Austria Vienna Univ Technol Vienna Austria Teknova AS Grimstad Norway

ISBN: (纸本)9781538607336

Event-based vision, as realized by bio-inspired Dynamic vision Sensors (DVS), is gaining more and more popularity due to its advantages of high temporal resolution, wide dynamic range and power efficiency at the same time. Potential applications include surveillance, robotics, and autonomous navigation under uncontrolled environment conditions. In this paper, we deal with event-based vision for 3D reconstruction of dynamic scene content by using two stationary DVS in a stereo configuration. We focus on a cooperative stereo approach and suggest an improvement over a previously published algorithm that reduces the measured mean error by over 50 percent. An available ground truth data set for stereo event data is utilized to analyze the algorithm's sensitivity to parameter variation and for comparison with competing techniques.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Multi-Class Multi-Movement Vehicle Counting Based on CenterTrack

Multi-Class Multi-Movement Vehicle Counting Based on CenterT...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kocur, Viktor Ftacnik, Milan Comenius Univ Fac Math Phys & Informat Bratislava Slovakia

ISBN: (纸本)9781665448994

In this paper we present our approach to the Track 1 of the 2021 AI City Challenge. The goal of the challenge track is to to analyse footage captured with traffic cameras by counting the number of vehicles performing various pre-defined motions of interest. Our approach is based on the CenterTrack object detection and tracking neural network used in conjunction with a simple IoU-based tracking algorithm. In the public evaluation server our system achieved the S1 score of 0.8449 placing it at the 8th place on the public leaderboard.

关键词： computer vision Tracking conferences Urban areas Neural networks Object detection computer architecture

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The Sightfield: Visualizing computer vision, and seeing its capacity to "see"

The Sightfield: Visualizing Computer Vision, and seeing its ...

引用

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

作者： Mann, Steve Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 1A1 Canada

ISBN: (纸本)9781479943098

computer vision is embedded in toilets, urinals, handwash faucets (e.g. Delta Faucet's 128 or 1024 pixel linear arrays), doors, lightswitches, thermostats, and many other objects that "watch" us. Camera-based motion sensing streetlights are installed throughout entire cities, making embedded vision ubiquitous. Technological advancement is leading to increased sensory and computational performance combined with miniaturization that is making vision sensors less visible. In that sense, computer vision is "seeing" better while it is becoming harder for us to see it. I will introduce and describe the concept of a "sight-field", a time-reversed lightfield that can be visualized with time-exposure photography, to make vision (i.e. the capacity to see) visible. In particular, I will describe a special wand that changes color when it is being observed. The wand has an array of light sources that each change color when being watched. The intensity of each light source increases in proportion to the degree to which it is observed. The wand is a surveillometer/sousveillometer array sensing, measuring, and making visible sur/sousveillance. Moving the wand through space, while tracking its exact 3D position in space, makes visible the otherwise invisible "rays of sight" that emenate from cameras. This capacity to sense, measure, and visualize vision, is useful in liability, insurance, safety, and risk assessment, as well as privacy/priveillance assessment, criminology, urban planning, design, and (sur/sous)veillance studies.

关键词： computer vision

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Privacy Leakage of Adversarial Training Models in Federated Learning Systems

Privacy Leakage of Adversarial Training Models in Federated ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Jingyang Chen, Yiran Li, Hai Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerable to privacy attacks. In this work, we further reveal this unsettling property of AT by designing a novel privacy attack that is practically applicable to the privacy-sensitive Federated Learning (FL) systems. Using our method, the attacker can exploit AT models in the FL system to accurately reconstruct users' private training images even when the training batch size is large. Code is available at https://***/zjysteven/PrivayAttack_AT_FL.

关键词： Training Deep learning Privacy computer vision conferences Neural networks Collaborative work

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Diagnosing Rarity in Human-object Interaction Detection

Diagnosing Rarity in Human-object Interaction Detection

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kilickaya, Mert Smeulders, Arnold QUvA Deep Vis Lab Amsterdam Netherlands

ISBN: (纸本)9781728193601

Human-object interaction (HOI) detection is a core task in computer vision. The goal is to localize all human-object pairs and recognize their interactions. An interaction defined by a tuple leads to a long-tailed visual recognition challenge since many combinations are rarely represented. The performance of the proposed models is limited especially for the tail categories, but little has been done to understand the reason. To that end, in this paper, we propose to diagnose rarity in HOI detection. We propose a three-step strategy, namely Detection, Identification and recognition where we carefully analyse the limiting factors by studying state-of-the-art models. Our findings indicate that detection and identification steps are altered by the interaction signals like occlusion and relative location, as a result limiting the recognition accuracy.

关键词： computer vision

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Attention! Stay Focus!

Attention! Stay Focus!

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Vo, Tu BridgeAI Inc Seoul South Korea

ISBN: (纸本)9781665448994

We develop a deep convolutional neural networks (CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images. Specifically, we develop a double attention network which consists of attentional encoders, triple locals and global local modules to effectively extract useful information from each image in the dual-pixels and select the useful information from each image and synthesize the final output image. We demonstrate the effectiveness of the proposed deblurring algorithm in terms of both qualitative and quantitative aspects by evaluating on the test set in the NTIRE 2021 Defocus Deblurring using Dual-pixel Images Challenge [1] [4].

关键词： Deep learning computer vision conferences computer architecture Feature extraction Cameras Decoding

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Land Cover Classification from Satellite Imagery With U-Net and Lovasz-Softmax Loss 31

Land Cover Classification from Satellite Imagery With U-Net ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Rakhlin, Alexander Davydow, Alex Nikolenko, Sergey Neuromat OU EE-10111 Tallinn Estonia

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The land cover classification task of the DeepGlohe Challenge presents significant obstacles even to state of the art segmentation models due to a small amount of data, incomplete and sometimes incorrect labeling, and highly imbalanced classes. In this work, we show an approach based on the U-Net architecture with the Lovcisz-Softmax loss that successfully alleviates these problems: we compare several different convolutional architectures for U-Net encoders.

关键词： Image segmentation Satellites computer architecture Training Task analysis computer vision Stochastic processes

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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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Sparklevision: Seeing the World through Random Specular Microfacets

SparkleVision: Seeing the World through Random Specular Micr...

引用

ieee conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Zhengdong Isola, Phillip Adelson, Edward H. MIT Cambridge MA 02139 USA

ISBN: (纸本)9781467367592

In this paper, we study the problem of reproducing the light from a single image of an object covered with random specular microfacets on the surface. We show that such reflectors can be interpreted as a randomized mapping from the lighting to the image. Such specular objects have very different optical properties from both diffuse surfaces and smooth specular objects like metals, so we design a special imaging system to robustly and effectively photograph them. We present simple yet reliable algorithms to calibrate the proposed system and do the inference. We conduct experiments to verify the correctness of our model assumptions and prove the effectiveness of our pipeline.

关键词： Seeing World Random Specular Microfacets

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Road detection with EOSResUNet and post vectorizing algorithm 31

Road detection with EOSResUNet and post vectorizing algorith...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Filin, Oleksandr Zapara, Anton Panchenko, Serhii EOS Data Analyt Menlo Pk CA 94027 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Object recognition on the satellite images is one of the most relevant and popular topics in the problem of pattern recognition. This was facilitated by many factors, such as a high number of satellites with high-resolution imagery, the significant development of computer vision, especially with a major breakthrough in the field of convolutional neural networks, a wide range of industry verticals for usage and still a quite empty market. Roads are one of the most popular objects for recognition. In this article, we want to present you the combination of work of neural network and postprocessing algorithm, due to which we get not only the coverage mask but also the vectors of all of the individual roads that are present in the image and can be used to address the higher-level tasks in the future. This approach was used to solve the DeepGlobe Road Extraction Challenge.

关键词： Roads Neural networks Task analysis Satellites computer vision Earth Observing System Data analysis

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