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检索条件"任意字段=29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 90 条记录，以下是21-30 订阅

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the Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action recognition 29

The Best of Both Worlds: Combining Data-independent and Data...

引用

29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Lan, Zhenzhong Yu, Shoou-I Yao, Dezhong Lin, Ming Raj, Bhiksha Hauptmann, Alexander

ISBN: (纸本)9781509014378

Motivated by the success of CNNs in object recognition on images, researchers are striving to develop CNN equivalents for learning video features. However, learning video features globally has proven to be quite a challenge due to the difficulty of getting enough labels, processing large-scale video data, and representing motion information. therefore, we propose to leverage effective techniques from both data-driven and data-independent approaches to improve action recognition system. Our contribution is three-fold. First, we explicitly show that local handcrafted features and CNNs share the same convolution-pooling network structure. Second, we propose to use independent subspace analysis (ISA) to learn descriptors for state-of-the-art handcrafted features. third, we enhance ISA with two new improvements, which make our learned descriptors significantly outperform the handcrafted ones. Experimental results on standard action recognition benchmarks show competitive performance.

关键词： Object recognition

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Human object interaction recognition using rate-invariant shape analysis of inter joint distances trajectories 29

Human object interaction recognition using rate-invariant sh...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Meng, Meng Drira, Hassen Daoudi, Mohamed Boonaert, Jacques Telecom Lille CRIStAL Lab UMR CNRS 9189 F-9189 Lille France Ecole Mines Douai Douai France

ISBN: (纸本)9781509014378

Human action recognition has emerged as one of the most challenging and active areas of research in the computer vision domain. In addition to pose variation and scale variability, high complexity of human motions and the variability of object interactions represent additional significant challenges. In this paper, we present an approach for human-object interaction modeling and classification. Towards that goal, we adopt relevant frame-level features;the inter-joint distances and joints-object distances. these proposed features are efficiently insensitive to position and pose variation. the evolution of the these distances in time is modeled by trajectories in a high dimension space and a shape analysis framework is used to model and compare the trajectories corresponding to human-object interaction in a Riemannian manifold. the experiments conducted following state-of-the-art settings and results demonstrate the strength of the proposed method. Using only the skeletal information, we achieve state-of-the-art classification results on the benchmark dataset.

关键词： Trajectories

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Background Subtraction using Local SVD Binary pattern 29

Background Subtraction using Local SVD Binary Pattern

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Guo, Lili Xu, Dan Qiang, Zhenping Yunnan Univ Sch Informat & Engn Kunming Peoples R China Southwest Forestry Univ Kunming Yunnan Peoples R China

ISBN: (纸本)9781509014378

Background subtraction is a basic problem for change detection in videos and also the first step of high-level computer vision applications. Most background subtraction methods rely on color and texture feature. However, due to illuminations changes in different scenes and affections of noise pixels, those methods often resulted in high false positives in a complex environment. To solve this problem, we propose an adaptive background subtraction model which uses a novel Local SVD Binary pattern (named LSBP) feature instead of simply depending on color intensity. this feature can describe the potential structure of the local regions in a given image, thus, it can enhance the robustness to illumination variation, noise, and shadows. We use a sample consensus model which is well suited for our LSBP feature. Experimental results on CDnet 2012 dataset demonstrate that our background subtraction method using LSBP feature is more effective than many state-of-the-art methods.

关键词： Image enhancement

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A Polarimetric thermal Database for Face recognition Research 29

A Polarimetric Thermal Database for Face Recognition Researc...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hu, Shuowen Short, Nathaniel J. Riggan, Benjamin S. Gordon, Christopher Gurton, Kristan P. thielke, Matthew Gurram, Prudhvi Chan, Alex L. US Army Res Lab 2800 Powder Mill Rd Adelphi MD 20783 USA Booz Allen Hamilton 8283 Greensboro Dr Mclean VA 20171 USA

ISBN: (纸本)9781509014378

We present a polarimetric thermal face database, the first of its kind, for face recognition research. this database was acquired using a polarimetric longwave infrared imager, specifically a division-of-time spinning achromatic retarder system. A corresponding set of visible spectrum imagery was also collected, to facilitate cross-spectrum (also referred to as heterogeneous) face recognition research. the database consists of imagery acquired at three distances under two experimental conditions: neutral/baseline condition, and expressions condition. Annotations (spatial coordinates of key fiducial points) are provided for all images. Cross-spectrum face recognition performance on the database is benchmarked using three techniques: partial least squares, deep perceptual mapping, and coupled neural networks.

关键词： Face recognition

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Pooling Faces: Template based Face recognition with Pooled Face Images 29

Pooling Faces: Template based Face Recognition with Pooled F...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hassner, Tal Masi, Iacopo Kim, Jungyeon Choi, Jongmoo Harel, Shai Natarajan, Prem Medioni, Gerard USC Inst Informat Sci Los Angeles CA 90007 USA Open Univ Israel Raanana Israel USC Inst Robot & Intelligent Syst Los Angeles CA USA

ISBN: (纸本)9781509014378

We propose a novel approach to template based face recognition. Our dual goal is to both increase recognition accuracy and reduce the computational and storage costs of template matching. To do this, we leverage on an approach which was proven effective in many other domains, but, to our knowledge, never fully explored for face images: average pooling of face photos. We show how (and why!) the space of a template's images can be partitioned and then pooled based on image quality and head pose and the effect this has on accuracy and template size. We perform extensive tests on the IJB-A and Janus CS2 template based face identification and verification benchmarks. these show that not only does our approach outperform published state of the art despite requiring far fewer cross template comparisons, but also, surprisingly, that image pooling performs on par with deep feature pooling.

关键词： Face recognition

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Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions 29

Extended DISFA Dataset: Investigating Posed and Spontaneous ...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Mavadati, Mohammad Sanger, Peyten Mahoor, Mohammad H. Univ Denver Dept Elect & Comp Engn 2390 S York St Denver CO 80208 USA

ISBN: (纸本)9781509014378

Automatic facial expression recognition (FER) is an important component of affect-aware technologies. Because of the lack of labeled spontaneous data, majority of existing automated FER systems were trained on posed facial expressions;however in real-world applications we deal with (subtle) spontaneous facial expression. this paper introduces an extension of DISFA, a previously released and well-accepted face dataset. Extended DISFA (DISFA+) has the following features: 1) it contains a large set of posed and spontaneous facial expressions data for a same group of individuals, 2) it provides the manually labeled frame-based annotations of 5-level intensity of twelve FACS facial actions, 3) it provides meta data (i.e. facial landmark points in addition to the self-report of each individual regarding every posed facial expression). this paper introduces and employs DISFA+, to analyze and compare temporal patterns and dynamic characteristics of posed and spontaneous facial expressions.

关键词： computer keyboards

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Towards Semantic Understanding of Surrounding Vehicular Maneuvers: A Panoramic vision-Based Framework for Real-World Highway Studies 29

Towards Semantic Understanding of Surrounding Vehicular Mane...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kristoffersen, Miklas S. Dueholm, Jacob V. Satzoda, Ravi K. Trivedi, Mohan M. Mogelmose, Andreas Moeslund, thomas B. Univ Calif San Diego San Diego CA 92103 USA Aalborg Univ Aalborg Denmark

ISBN: (纸本)9781509014378

this paper proposes the use of multiple low-cost visual sensors to obtain a surround view of the ego-vehicle for semantic understanding. A multi-perspective view will assist the analysis of naturalistic driving studies (NDS), by automating the task of data reduction of the observed sequences into events. A user-centric vision-based framework is presented using a vehicle detector and tracker in each separate perspective. Multi-perspective trajectories are estimated and analyzed to extract 14 different events, including potential dangerous behaviors such as overtakes and cut-ins. the system is tested on ten sequences of real-world data collected on U.S. highways. the results show the potential use of multiple low-cost visual sensors for semantic understanding around the ego-vehicle.

关键词： Vehicles

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Fusing Aligned and Non-Aligned Face Information for Automatic Affect recognition in the Wild: A Deep Learning Approach 29

Fusing Aligned and Non-Aligned Face Information for Automati...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kim, Bo-Kyeong Dong, Suh-Yeon Roh, Jihyeon Kim, Geonmin Lee, Soo-Young Korea Adv Inst Sci & Technol Computat NeuroSyst Lab CNSL Daejeon South Korea

ISBN: (纸本)9781509014378

Face alignment can fail in real-world conditions, negatively impacting the performance of automatic facial expression recognition (FER) systems. In this study, we assume a realistic situation including non-alignable faces due to failures in facial landmark detection. Our proposed approach fuses information about non-aligned and aligned facial states, in order to boost FER accuracy and efficiency. Six experimental scenarios using discriminative deep convolutional neural networks (DCNs) are compared, and causes for performance differences are identified. To handle non-alignable faces better, we further introduce DCNs that learn a mapping from non-aligned facial states to aligned ones, alignment-mapping networks (AMNs). We show that AMNs represent geometric transformations of face alignment, providing features beneficial for FER. Our automatic system based on ensembles of the discriminative DCNs and the AMNs achieves impressive results on a challenging database for FER in the wild.

关键词： Mapping

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Multimodal Multi-stream Deep Learning for Egocentric Activity recognition 29

Multimodal Multi-stream Deep Learning for Egocentric Activit...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Song, Sibo Chandrasekhar, Vijay Mandal, Bappaditya Li, Liyuan Lim, Joo-Hwee Babu, Giduthuri Sateesh San, Phyo Phyo Cheung, Ngai-Man Singapore Univ Technol & Design Singapore Singapore Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781509014378

In this paper, we propose a multimodal multi-stream deep learning framework to tackle the egocentric activity recognition problem, using both the video and sensor data. First, we experiment and extend a multi-stream Convolutional Neural Network to learn the spatial and temporal features from egocentric videos. Second, we propose a multi-stream Long Short-Term Memory architecture to learn the features from multiple sensor streams (accelerometer, gyroscope, etc.). third, we propose to use a two-level fusion technique and experiment different pooling techniques to compute the prediction results. Experimental results using a multimodal egocentric dataset show that our proposed method can achieve very encouraging performance, despite the constraint that the scale of the existing egocentric datasets is still quite limited.

关键词： Memory architecture

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Embedded vision System for Atmospheric Turbulence Mitigation 29

Embedded Vision System for Atmospheric Turbulence Mitigation

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Deshmukh, Ajinkya Bhosale, Gaurav Medasani, Swarup Reddy, Karthik Hemanthakumar, P. Chandrasekhar, A. Kirankumar, P. Vijayasagar, K. Uurmi Syst Pvt Ltd Hyderabad Andhra Pradesh India

ISBN: (纸本)9781509014378

Outdoor surveillance systems that involve farfield operations often encounter atmospheric turbulence perturbations due to a series of randomized reflections and refraction effecting incoming light rays. the resulting distortions make it hard to discriminate between true moving objects and turbulence induced motion. Current algorithms are not effective in detecting true moving objects in the scene and also rely on computationally complex warping methods. In this paper, we describe a real time embedded solution connected with traditional cameras to both rectify turbulence distortions and reliably detect and track true moving targets. Our comparisons with other methods shows better turbulence rectification with less false and miss detections. FPGA-DSP based embedded realization of our algorithm achieves nearly 15x speed-up along with lesser memory requirement over a quad core PC implementation. the proposed system is suitable for persistence surveillance systems and optical sight devices.

关键词： Atmospheric turbulence

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