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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1181-1190 订阅

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Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks 32

Real-Time 6DOF Pose Relocalization for Event Cameras with St...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Anh Nguyen Thanh-Toan Do Caldwell, Darwin G. Tsagarakis, Nikos G. Ist Italiano Tecnol Genoa Italy Univ Liverpool Liverpool Merseyside England AIOZ Pte Ltd Singapore Singapore

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

We present a new method to relocalize the 6DOF pose of an event camera solely based on the event stream. Our method first creates the event image from a list of events that occurs in a very short time interval, then a Stacked Spatial LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is composed of a CNN to learn deep features from the event images and a stack of LSTM to learn spatial dependencies in the image feature space. We show that the spatial dependency plays an important role in the relocalization task with event images and the SP-LSTM can effectively learn this information. The extensively experimental results on a publicly available dataset show that our approach outperforms recent state-of-the-art methods by a substantial margin, as well as generalizes well in challenging training/testing splits. The source code and trained models are available at https://***/ nqanh/pose_relocalization.

关键词： Cameras Robot vision systems Three-dimensional displays Geometry Task analysis Streaming media computer vision

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Temporal Hockey Action recognition via Pose and Optical Flows 32

Temporal Hockey Action Recognition via Pose and Optical Flow...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cai, Zixi Neher, Helmut Vats, Kanav Clausi, David A. Zelek, John Tsinghua Univ Beijing Peoples R China Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781728125060

In this paper, a novel two-stream architecture has been designed to improve action recognition accuracy for hockey using three main components. First, pose is estimated via the Part Affinity Fields model to extract meaningful cues from the player. Second, optical flow (using LiteFlownet) is used to extract temporal features. Third, pose and optical flow streams are fused and passed to fully-connected layers to estimate the hockey players action. A novel publicly available dataset named HARPET (Hockey Action recognition Pose Estimation, Temporal) was created, composed of sequences of annotated actions and pose of hockey players including their hockey sticks as an extension of human body pose. Three contributions are recognized. (1) The novel two-stream architecture achieves 85% action recognition accuracy, with the inclusion of optical flows increasing accuracy by about 10%. Thus, demonstrating the complementary nature of pose estimation and optical flow. (2) The unique localization of hand-held objects (e.g., hockey sticks) as part of pose increases accuracy by about 13%. (3) For pose estimation, a bigger and more general dataset, MSCOCO, is successfully used for transfer learning to a smaller and more specific dataset, HARPET, achieving a PCKh of 87%.

关键词： Optical flows

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Characterizing the Variability in Face recognition Accuracy Relative to Race 32

Characterizing the Variability in Face Recognition Accuracy ...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Krishnapriya, K. S. Vangara, Kushal King, Michael C. Albiero, Vitor Bowyer, Kevin Florida Inst Technol Melbourne FL 32901 USA Univ Notre Dame Notre Dame IN 46556 USA

ISBN: (纸本)9781728125060

Many recent news headlines have labeled face recognition technology as "biased" or "racist". We report on a methodical investigation into differences in face recognition accuracy between African American and Caucasian image cohorts of the MORPH dataset. We find that, for allfour matchers considered, the impostor and the genuine distributions are statistically significantly different between cohorts. For a fixed decision threshold, the African-American image cohort has a higher false match rate and a lower false non-match rate. ROC curves compare verification rates at the same false match rate, but the different cohorts achieve the same false match rate at different thresholds. This means that ROC comparisons are not relevant to operational scenarios that use a fixed decision threshold. We show that, for the ResNet matcher, the two cohorts have approximately equal separation of impostor and genuine distributions. Using ICAO compliance as a standard of image quality, we find that the initial image cohorts have unequal rates of good quality images. The ICAO-compliant subsets of the original image cohorts show improved accuracy, with the main effect being to reducing the low-similarity tail of the genuine distributions.

关键词： Face recognition

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Evading Face recognition via Partial Tampering of Faces 32

Evading Face Recognition via Partial Tampering of Faces

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Majumdar, Puspita Agarwal, Akshay Singh, Richa Vatsa, Mayank IIIT Delhi Delhi India

ISBN: (纸本)9781728125060

Advancements in machine learning and deep learning techniques have led to the development of sophisticated and accurate face recognition systems. However, for the past few years, researchers are exploring the vulnerabilities of these systems towards digital attacks. Creation of digitally altered images has become an easy task with the availability of various image editing tools and mobile application such as Snapchat. Morphing based digital attacks are used to elude and gain the identity of legitimate users by fooling the deep networks. In this research, partial face tampering attack is proposed, where facial regions are replaced or morphed to generate tampered samples. Face verification experiments performed using two state-of-the-art face recognition systems, VGG-Face and OpenFace on the CMU- MultiPlE dataset indicates the vulnerability of these systems towards the attack. Further, a Partial Face Tampering Detection (PFTD) network is proposedfor the detection of the proposed attack. The network captures the inconsistencies among the original and tampered images by combining the raw and high frequency information of the input images for the detection of tampered images. The proposed network surpasses the performance of the existing baseline deep neural networks for tampered image detection.

关键词： Face recognition

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WiFi and vision Multimodal Learning for Accurate and Robust Device -Free Human Activity recognition 32

WiFi and Vision Multimodal Learning for Accurate and Robust ...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zou, Han Yang, Jianfei Das, Hari Prasanna Liu, Huihan Zhou, Yuxun Spanos, Costas J. Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781728125060

Human activity recognition plays an indispensable role in a myriad of emerging applications in context-aware services. Accurate activity recognition systems usually require the user to carry mobile or wearable devices, which is inconvenient for long term usage. In this paper, we design WiVi, a novel human activity recognition scheme that is able to identify common human activities in an accurate and device free manner via multimodal machine learning using only commercial WiFi-enabled IoT devices and camera. For sensing using WiFi, a new platform is developed to extract fine-grained WiFi channel information and transform them into WiFi frames. A tailored convolutional neural network model is designed to extract high-level representative features among the WiFi frames in order to provide human activity estimation. We utilized a variant of C3D model for activity sensing using vision. Following this, WiVi performs multimodal fusion at the decision level to combine the strength of WiFi and vision by constructing an ensembled DNN model. Extensive experiments are conducted in an indoor environment, demonstrating that WiVi achieves 97.5% activity recognition accuracy and is robust under unfavorable situations, as each modality provides the complementary sensing when the other faces its limiting conditions.

关键词： Information services

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AnonymousNet: Natural Face De-Identification with Measurable Privacy 32

AnonymousNet: Natural Face De-Identification with Measurable...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Tao Lin, Lei Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Univ Rochester Goergen Inst Data Sci Rochester NY 14627 USA

ISBN: (纸本)9781728125060

With billions of personal images being generated from social media and cameras of all sorts on a daily basis, security and privacy are unprecedentedly challenged. Although extensive attempts have been made, existing face image de-identification techniques are either insufficient in photo-reality or incapable of balancing privacy and usability qualitatively and quantitatively, i.e., they fail to answer counterfactual questions such as "is it private now?", "how private is it?", and "can it be more private?" In this paper, we propose a novel framework called AnonymousNet, with an effort to address these issues systematically, balance usability, and enhance privacy in a natural and measurable manner. The framework encompasses four stages: facial attribute estimation, privacy-metric-oriented face obfuscation, directed natural image synthesis, and adversarial perturbation. Not only do we achieve the state-of-the-arts in terms of image quality and attribute prediction accuracy, we are also the first to show that facial privacy is measurable, can be factorized, and accordingly be manipulated in a photo-realistic fashion to fulfill different requirements and application scenarios. Experiments further demonstrate the effectiveness of the proposed framework.

关键词： computer vision

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Deep Anomaly Detection for Generalized Face Anti-Spoofing 32

Deep Anomaly Detection for Generalized Face Anti-Spoofing

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Perez-Cabo, Daniel Jimenez-Cabello, David Costa-Pazo, Artur Lopez-Sastre, Roberto J. Gradiant UVigo Vigo Spain Gradiant Vigo Spain Univ Alcala Alcala De Henares Spain

ISBN: (纸本)9781728125060

Face recognition has achieved unprecedented results, surpassing human capabilities in certain scenarios. However, these automatic solutions are not ready for production because they can be easily fooled by simple identity impersonation attacks. And although much effort has been devoted to develop face anti-spoofing models, their generalization capacity still remains a challenge in real scenarios. In this paper, we introduce a novel approach that reformulates the Generalized Presentation Attack Detection (GPAD) problem from an anomaly detection perspective. Technically, a deep metric learning model is proposed, where a triplet focal loss is used as a regularization for a novel loss coined "metric-softmax", which is in charge of guiding the learning process towards more discriminative feature representations in an embedding space. Finally, we demonstrate the benefits of our deep anomaly detection architecture, by introducing a few-shot a posteriori probability estimation that does not need any classifier to be trained on the learned features. We conduct extensive experiments using the GRAD-GPAD framework that provides the largest aggregated dataset forface GPAD. Results confirm that our approach is able to outperform all the state-of-the-art methods by a considerable margin.

关键词： Face recognition

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Face recognition Algorithm Bias: Performance Differences on Images of Children and Adults 32

Face Recognition Algorithm Bias: Performance Differences on ...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Srinivas, Nisha Ricanek, Karl Michalski, Dana Bolme, David S. King, Michael Univ North Carolina Wilmington Wilmington NC 28403 USA Def Sci & Technol Grp Edinburgh SA Australia Oak Ridge Natl Lab Oak Ridge TN USA Florida Inst Technol Melbourne FL 32901 USA

ISBN: (纸本)9781728125060

In this work, we examine if current state-of-the-art deep learning face recognition systems exhibit a negative bias (i.e., poorer performance) for children when compared to the performance obtained on adults. The systems selected for this work are five top performing commercial-off-the-shelf-face recognition systems, two government-off-the-shelf face recognition systems, and one open-source face recognition solution. The datasets used to evaluate the performance of the systems are both unconstrained in age, pose, illumination, and expression and are publicly available. These datasets are indicative of photo journalistic face datasets published and evaluated on, over the last few years. Ourfindings show a negative bias for each algorithm on children. Genuine and imposter distributions highlight the performance bias between the datasets further supporting the need for a deeper investigation into algorithm bias as a function of age cohorts. To combat the performance decline on the child demographic, several score-level fusion strategies were evaluated. This work identifies the best score-level fusion technique for the child demographic.

关键词： Face recognition

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Weakly Supervised Person Re-Identification 32

Weakly Supervised Person Re-Identification

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Meng, Jingke Wu, Sheng Zheng, Wei-Shi Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China Accuvis Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728132938

In the conventional person re-id setting, it is assumed that the labeled images are the person images within the bounding box for each individual;this labeling across multiple nonoverlapping camera views from raw video surveillance is costly and time-consuming. To overcome this difficulty, we consider weakly supervised person re-id modeling. The weak setting refers to matching a target person with an untrimmed gallery video where we only know that the identity appears in the video without the requirement of annotating the identity in any frame of the video during the training procedure. Hence, for a video, there could be multiple video-level labels. We cast this weakly supervised person re-id challenge into a multi-instance multi-label learning (MIML) problem. In particular, we develop a Cross-View MIML (CV-MIML) method that is able to explore potential intraclass person images from all the camera views by incorporating the intra-bag alignment and the cross-view bag alignment. Finally, the CV-MIML method is embedded into an existing deep neural network for developing the Deep Cross-View MIML (Deep CV-MIML) model. We have performed extensive experiments to show the feasibility of the proposed weakly supervised setting and verify the effectiveness of our method compared to related methods on four weakly labeled datasets.

关键词： And Body Pose Categorization Face Gesture recognition: Detection Retrieval Scene Analysis and Understanding

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Cross-stream Selective Networks for Action recognition 32

Cross-stream Selective Networks for Action Recognition

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Pan, Bowen Sun, Jiankai Lin, Wuwei Wang, Limin Lin, Weiyao Shanghai Jiao Tong Univ Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728125060

Combining multiple information streams has shown obvious improvements in video action recognition. Most existing works handle each stream independently or perform a simple combination on temporally simultaneous samples in multi-streams, which fails to make full use of the stream wise complementary property due to the negligence of the temporal pattern gaps among streams. In this paper, we propose a cross-stream selective network (CSN) to properly integrate and evaluate information in multi-streams. The proposed CSN first introduces a local selective-sampling module (LSM), which can find asynchronous correspondences among streams and construct high-correlated sample groups across multiple information streams. This LSM can effectively deal with the temporal dis-alignment among different streams, leading to a better integration of cross-stream information. We further introduce a global adaptive weighting module (GAM). It adaptively evaluates the importance weights for each cross-stream sample group and selects temporally more important ones in action recognition. With the integration of cross-stream information, our GAM can obtain more reasonable importance than the existing single-stream weighting schemes. Extensive experiments on benchmark datasets of UCF101 and HMDB51 demonstrate the effectiveness of our approach over previous state-of-the-art methods.

关键词： computer vision

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