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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是1211-1220 订阅

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CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks

CIAGAN: Conditional Identity Anonymization Generative Advers...

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conference on computer vision and pattern recognition (cvpr)

作者： Maxim Maximov Ismail Elezi Laura Leal-Taixé Technical University Munich University of Venice

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity. We propose and develop CIAGAN, a model for image and video anonymization based on conditional generative adversarial networks. Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos that can be used for any computer vision task, such as detection or tracking. Unlike previous methods, we have full control over the de-identification (anonymization) procedure, ensuring both anonymization as well as diversity. We compare our method to several baselines and achieve state-of-the-art results. To facilitate further research, we make available the code and the models at https://***/dvl-tum/ciagan.

关键词： Face Generators computer vision Task analysis Generative adversarial networks Data privacy Gallium nitride

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Proceedings - 2019 ieee/CVF conference on computer vision and pattern recognition, cvpr 2019

Proceedings - 2019 IEEE/CVF Conference on Computer Vision an...

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32nd ieee/CVF conference on computer vision and pattern recognition, cvpr 2019

ISBN: (纸本)9781728132938

The proceedings contain 1294 papers. The topics discussed include: finding task-relevant features for few-shot learning by category traversal;edge-labeling graph neural network for few-shot learning;generating classification weights with GNN denoising autoencoders for few-shot learning;kervolutional neural networks;why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem;on the structural sensitivity of deep convolutional networks to the directions of fourier basis functions;hardness-aware deep metric learning;auto-deeplab: hierarchical neural architecture search for semantic image segmentation;striking the right balance with uncertainty;and SDRSAC: semidefinite-based randomized approach for robust point cloud registration without correspondences.

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Neurodata Lab’s approach to the Challenge on computer vision for Physiological Measurement

Neurodata Lab’s approach to the Challenge on Computer Visio...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Mikhail Artemyev Marina Churikova Mikhail Grinenko Olga Perepelkina Neurodata Lab LLC Miami USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper introduces the Neurodata Lab's approach presented at the 1st Challenge on Remote Physiological Signal Sensing (RePSS) organized within cvpr2020. The RePSS challenge was focused on measuring the average heart rate from color facial videos, which is one of the most fundamental problems in the field of computer vision. Our deep learning-based approach includes 3D spatiotemporal attention convolutional neural network for photoplethysmogram extraction and 1D convolutional neural network pre-trained on synthetic data for time series analysis. It provides state-of-the-art results outperforming those of other participants on a mixture of VIPL and OBF databases: MAE=6.94 (12.3% improvement compared to the top-2 result), RMSE=10.68 (24.6% improvement), Pearson R = 0.755 (28.2% improvement).

关键词： Heart rate Three-dimensional displays Kernel Videos Estimation Face Convolution

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H+O: Unified egocentric recognition of 3D hand-object poses and interactions 32

H+O: Unified egocentric recognition of 3D hand-object poses ...

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32nd ieee/CVF conference on computer vision and pattern recognition, cvpr 2019

作者： Tekin, Bugra Bogo, Federica Pollefeys, Marc Microsoft United States ETH Zurich Switzerland

ISBN: (纸本)9781728132938

We present a unified framework for understanding 3D hand and object interactions in raw image sequences from egocentric RGB cameras. Given a single RGB image, our model jointly estimates the 3D hand and object poses, models their interactions, and recognizes the object and action classes with a single feed-forward pass through a neural network. We propose a single architecture that does not rely on external detection algorithms but rather is trained end-to-end on single images. We further merge and propagate information in the temporal domain to infer interactions between hand and object trajectories and recognize actions. The complete model takes as input a sequence of frames and outputs per-frame 3D hand and object pose predictions along with the estimates of object and action categories for the entire sequence. We demonstrate state-of-the-art performance of our algorithm even in comparison to the approaches that work on depth data and ground-truth annotations. © 2019 ieee.

关键词： Gesture recognition

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M2SGD: Learning to Learn Important Weights

M2SGD: Learning to Learn Important Weights

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Nicholas I-Hsien Kuo Mehrtash Harandi Nicolas Fourrier Christian Walder Gabriela Ferraro Hanna Suominen RSCS The Australian National University ECSE Monash University Australia Pole Universitaire Leonard de Vinci Paris La Defense France

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Meta-learning concerns rapid knowledge acquisition. One popular approach cast optimisation as a learning problem and it has been shown that learnt neural optimisers updated base learners more quickly than their handcrafted counterparts. In this paper, we learn an optimisation rule that sparsely updates the learner parameters and removes redundant weights. We present Masked Meta-SGD (M 2 SGD), a neural optimiser which is not only capable of updating learners quickly, but also capable of removing 83.71% weights for *** release our codes at https://***/Nic5472K/CLvision2020_cvpr_M2SGD.

关键词： Training Optimization Recurrent neural networks computer architecture Task analysis Image recognition Australia

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Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification

Attribute-guided Feature Extraction and Augmentation Robust ...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Chaoran Zhuge Yujie Peng Yadong Li Jiangbo Ai Junru Chen University of Science and Technology of China School of Computer Science and Engineering Beihang university University of Electronic Science and Technology of China Xidian University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Vehicle re-identification is one of the core technologies of intelligent transportation systems and smart cities, but large intra-class diversity and inter-class similarity poses great challenges for existing method. In this paper, we propose a multi-guided learning approach which utilizing the information of attributes and meanwhile introducing two novel random augments to improve the robustness during training. What's more, we propose an attribute constraint method and group re-ranking strategy to refine matching results. Our method achieves mAP of 66.83% and rank-1 accuracy 76.05% in the cvpr 2020 AI City Challenge.

关键词： Feature extraction Training Image color analysis Robustness Cameras Task analysis Automobiles

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Small object bird detection in infrared drone videos using mask R-CNN deep learning

Small object bird detection in infrared drone videos using m...

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2020 Imaging and Multimedia Analytics in a Web and Mobile World conference, IMAWM 2020

作者： Kassim, Yasmin M. Byrne, Michael E. Burch, Cristy Mote, Kevin Hardin, Jason Larsen, David R. Palaniappan, Kannappan Department of Electrical Engineering and Computer Science University of Missouri-Columbia ColumbiaMO65201 United States School of Natural Resources University of Missouri-Columbia ColumbiaMO65201 United States Texas Parks and Wildlife Department AustinTX78744 United States

A reliable method to estimate population sizes of wild turkeys (Meleagris gallopavo) using unmanned aerial vehicles and thermal video imaging data collected at several field sites in Texas is described. Automating the data processing of airborne survey videos provides a fast and reproducible way to count wild turkeys for wildlife management and conservation. A deep learning semantic segmentation pipeline is developed to detect and count roosting Rio-Grande wild turkeys (M.g. intermedia) which appear as small faint objects in drone-based thermal IR videos. The proposed approach to detect roosting turkeys that appear as small objects, relies on Mask R-CNN, a deep architecture semantic segmentation algorithm. This is followed by a post-processing data association and filtering (DAF) process for counting the number of roosting birds. DAF was used to eliminate false positives like rocks and other small bright objects, which often have noisy detections across temporally adjacent video frames, that can be filtered using appearance association and distance-based gating across time. Transfer learning was used to train the Mask R-CNN network by initializing using ImageNet weights. Drone-based thermal IR videos are extremely challenging due to the complexity of the natural environment including weather effects, occlusion of birds, terrain, trees, complex tree shapes, rocks, water and thermal inversion. The transect videos were collected at night at several times and altitudes to optimize data collection opportunities without disturbing the roosting turkeys. Preliminary performance evaluation using 280 video frames is promising. © 2020, society for Imaging Science and Technology.

关键词： Drones

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Anomaly candidate identification and starting time estimation of vehicles from traffic videos 32

Anomaly candidate identification and starting time estimatio...

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32nd ieee/CVF conference on computer vision and pattern recognition Workshops, cvprW 2019

作者： Wang, Gaoang Yuan, Xinyu Zhang, Aotian Hsu, Hung-Min Hwang, Jenq-Neng Department of Electrical and Computer Engineering University of Washington United States Research Center for Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)9781728125060

Anomaly event detection on road traffic has been a challenging field mainly due to lack of training data and a wide variety of anomaly cases. In this paper, we propose a novel two-stage framework for anomaly event detection in road traffic based on anomaly candidate identification and starting time estimation of vehicles. First, we use Gaussian mixture models (GMMs) to generate the foreground mask and background image to identify the anomaly candidates. Foreground mask is used to produce the region of interest (ROI) to filter out the noise from the object detector, YOLOv3, in the background image. Then, we apply the TrackletNet Tracker (TNT) to extract the trajectory of anomaly candidate to estimate the anomaly starting time. Experimental results, with achieved S3 score performance of 93.62%, on the Track 3 testing set of cvpr AI City Challenge 2019 City Flow dataset, show the effectiveness of the proposed framework and its robustness in different real scenes. © 2019 ieee computer society. All rights reserved.

关键词： Object detection

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Multi-view vehicle re-identification using temporal attention model and metadata re-ranking 32

Multi-view vehicle re-identification using temporal attentio...

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32nd ieee/CVF conference on computer vision and pattern recognition Workshops, cvprW 2019

作者： Huang, Tsung-Wei Cai, Jiarui Yang, Hao Hsu, Hung-Min Hwang, Jenq-Neng Department of Electrical and Computer Engineering University of Washington United States Department of Civil and Environmental Engineering University of Washington United States Research Center for Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)9781728125060

Object re-identification (ReID) is an arduous task which requires matching an object across different non-overlapping camera views. Recently, many researchers are working on person ReID by taking advantages of appearance, human pose, temporal constraints, etc. However, vehicle ReID is even more challenging because vehicles have fewer discriminant features than human due to viewpoint orientation, changes in lighting condition and inter-class similarity. In this paper, we propose a viewpoint-aware temporal attention model for vehicle ReID utilizing deep learning features extracted from consecutive frames with vehicle orientation and metadata attributes (i.e., type, brand, color) being taken into consideration. In addition, re-ranking with soft decision boundary is applied as post-processing for result refinement. The proposed method is evaluated on cvpr AI City Challenge 2019 dataset, achieving mAP of 79.17% with the second place ranking in the competition. © 2019 ieee computer society. All rights reserved.

关键词： Vehicles

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Multi-camera tracking of vehicles based on deep features re-id and trajectory-based camera link models 32

Multi-camera tracking of vehicles based on deep features re-...

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32nd ieee/CVF conference on computer vision and pattern recognition Workshops, cvprW 2019

作者： Hsu, Hung-Min Huang, Tsung-Wei Wang, Gaoang Cai, Jiarui Lei, Zhichao Hwang, Jenq-Neng Department of Electrical and Computer Engineering University of Washington United States Research Center for Information Technology Innovation Academia Sinica Taiwan

ISBN: (纸本)9781728125060

Due to the exponential growth of traffic camera net- works, the need for multi-camera tracking (MCT) for in- telligent transportation has received more and more at- tention. The challenges of MCT include similar vehicle models, significant feature variation in different orienta- tions, color variation of the same car due to lighting con- ditions, small object sizes and frequent occlusion, as well as the varied resolutions of videos. In this work, we pro- pose an MCT system, which combines single-camera track- ing (SCT) and inter-camera tracking (ICT) which includes trajectory-based camera link model and deep feature re- *** SCT, we use a TrackletNet Tracker (TNT), which effectively generates the moving trajectories of all de- tected vehicles by exploiting temporal and appearance in- formation of multiple tracklets that are created by associat- ing bounding boxes of detected vehicles. The tracklets are generated based on CNN feature matching and intersection- over-union (IOU) in every single-camera view. In terms of deep feature re-identification, we exploit the temporal at- tention model to extract the most discriminant feature of each trajectory. In addition, we propose the trajectory- based camera link models with order constraint to effi- ciently leverage the spatial and temporal information for ICT. The proposed method is evaluated on cvpr AI City Challenge2019 City Flow dataset, achieving IDF1 70.59%, which outperforms competing methods. © 2019 ieee computer society. All rights reserved.

关键词： Vehicles

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