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Cross-view Action Modeling, Learning and recognition 27

Cross-view Action Modeling, Learning and Recognition

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

作者： Wang, Jiang Nie, Xiaohan Xia, Yin Wu, Ying Zhu, Song-Chun Northwestern Univ Evanston IL 60208 USA Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781479951178

Existing methods on video-based action recognition are generally view-dependent, i. e., performing recognition from the same views seen in the training data. We present a novel multiview spatio-temporal AND-OR graph (MSTAOG) representation for cross-view action recognition, i. e., the recognition is performed on the video from an unknown and unseen view. As a compositional model, MST-AOG compactly represents the hierarchical combinatorial structures of cross-view actions by explicitly modeling the geometry, appearance and motion variations. this paper proposes effective methods to learn the structure and parameters of MST-AOG. the inference based on MST-AOG enables action recognition from novel views. the training of MST-AOG takes advantage of the 3D human skeleton data obtained from Kinect cameras to avoid annotating enormous multi-view video frames, which is error-prone and time-consuming, but the recognition does not need 3D information and is based on 2D video input. A new Multiview Action3D dataset has been created and will be released. Extensive experiments have demonstrated that this new action representation significantly improves the accuracy and robustness for cross-view action recognition on 2D videos.

关键词： pattern recognition

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Human Action recognition by Representing 3D Skeletons as Points in a Lie Group 27

Human Action Recognition by Representing 3D Skeletons as Poi...

引用

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

作者： Vemulapalli, Raviteja Arrate, Felipe Chellappa, Rama Univ Maryland UMIACS Ctr Automat Res College Pk MD 20742 USA

ISBN: (纸本)9781479951178

Recently introduced cost-effective depth sensors coupled with the real-time skeleton estimation algorithm of Shotton et al. [16] have generated a renewed interest in skeleton-based human action recognition. Most of the existing skeleton-based approaches use either the joint locations or the joint angles to represent a human skeleton. In this paper, we propose a new skeletal representation that explicitly models the 3D geometric relationships between various body parts using rotations and translations in 3D space. Since 3D rigid body motions are members of the special Euclidean group SE(3), the proposed skeletal representation lies in the Lie group SE(3) x...xSE(3), which is a curved manifold. Using the proposed representation, human actions can be modeled as curves in this Lie group. Since classification of curves in this Lie group is not an easy task, we map the action curves from the Lie group to its Lie algebra, which is a vector space. We then perform classification using a combination of dynamic time warping, Fourier temporal pyramid representation and linear SVM. Experimental results on three action datasets show that the proposed representation performs better than many existing skeletal representations. the proposed approach also outperforms various state-of-the-art skeleton-based human action recognition approaches.

关键词： Action recognition Lie Groups Special Euclidean Group

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Describing Textures in the Wild 27

Describing Textures in the Wild

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

作者： Cimpoi, Mircea Maji, Subhransu Kokkinos, Iasonas Mohamed, Sammy Vedaldi, Andrea Univ Oxford Oxford OX1 2JD England Toyota Technol Inst Nagoya Aichi Japan Ecole Cent Paris INRIA Saclay Paris France

ISBN: (纸本)9781479951178

patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected "in the wild". the resulting Describable Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and Deep Convolutional-network Activation Features (DeCAF), and show that surprisingly, they both outperform specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that our describable attributes are excellent texture descriptors, transferring between datasets and tasks;in particular, combined with IFV and DeCAF, they significantly outperform the state-of-the-art by more than 10% on both FMD and Kth-TIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.

关键词： Fisher Vector attribute convolutional neural network material recognition texture

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Secure Fingerprint Matching With Generic Local Structures

Secure Fingerprint Matching With Generic Local Structures

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

作者： Morse, Matthew Hartloff, Jesse Effland, thomas Schuler, Jim Cordaro, Jennifer Tulyakov, Sergey Rudra, Atri Govindaraju, Venu SUNY Buffalo Buffalo NY 14260 USA

ISBN: (纸本)9781479943098

In this work we evaluate the performance of generic local structures as template points for secure fingerprint matching. We present a generic template structure called an n-gon that derives from a set of n neighboring minutiae points. We secure templates consisting of sets of n-gons using the fuzzy vault construct to obfuscate the data. We report the matching performance of our system in terms of the ZeroFAR for comparison with other systems. We also briefly describe a keyed version of our system for comparison with secure systems that utilize a secret user key.

关键词： fingerprint fuzzy vault security Fingerprint recognition Fingerprints n gon Secure jigs(templates) Security

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Human Action recognition Based on Context-Dependent Graph Kernels 27

Human Action Recognition Based on Context-Dependent Graph Ke...

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

作者： Wu, Baoxin Yuan, Chunfeng Hu, Weiming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9781479951178

Graphs are a powerful tool to model structured objects, but it is nontrivial to measure the similarity between two graphs. In this paper, we construct a two-graph model to represent human actions by recording the spatial and temporal relationships among local features. We also propose a novel family of context-dependent graph kernels (CGKs) to measure similarity between graphs. First, local features are used as the vertices of the two-graph model and the relationships among local features in the intra-frames and inter-frames are characterized by the edges. then, the proposed CGKs are applied to measure the similarity between actions represented by the two-graph model. Graphs can be decomposed into numbers of primary walk groups with different walk lengths and our CGKs are based on the context-dependent primary walk group matching. Taking advantage of the context information makes the correctly matched primary walk groups dominate in the CGKs and improves the performance of similarity measurement between graphs. Finally, a generalized multiple kernel learning algorithm with a proposed l12-norm regularization is applied to combine these CGKs optimally together and simultaneously train a set of action classifiers. We conduct a series of experiments on several public action datasets. Our approach achieves a comparable performance to the state-of-the-art approaches, which demonstrates the effectiveness of the two-graph model and the CGKs in recognizing human actions.

关键词： Human action recognition context-dependent graph kernels generalized multiple kernel learning two-graph model

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Gesture recognition in Ego-Centric Videos using Dense Trajectories and Hand Segmentation

Gesture Recognition in Ego-Centric Videos using Dense Trajec...

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

作者： Baraldi, Lorenzo Paci, Francesco Serra, Giuseppe Benini, Luca Cucchiara, Rita Univ Modena & Reggio Emilia Dipartimento Ingn Enzo Ferrari Modena MO Italy Univ Bologna DEI Bologna Italy Swiss Fed Inst Technol Integrated Syst Lab Zurich Switzerland

ISBN: (纸本)9781479943098

We present a novel method for monocular hand gesture recognition in ego-vision scenarios that deals with static and dynamic gestures and can achieve high accuracy results using a few positive samples. Specifically, we use and extend the dense trajectories approach that has been successfully introduced for action recognition. Dense features are extracted around regions selected by a new hand segmentation technique that integrates superpixel classification, temporal and spatial coherence. We extensively test our gesture recognition and segmentation algorithms on public datasets and propose a new dataset shot with a wearable camera. In addition, we demonstrate that our solution can work in near real-time on a wearable device.

关键词： Gesture recognition

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vision on Wheels: Looking at Driver, Vehicle, and Surround for On-Road Maneuver Analysis

Vision on Wheels: Looking at Driver, Vehicle, and Surround f...

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

作者： Ohn-Bar, Eshed Tawari, Ashish Martin, Sujitha Trivedi, Mohan M. Univ Calif San Diego Comp Vis & Robot Res Lab San Diego CA 92103 USA

ISBN: (纸本)9781479943098

Automotive systems provide a unique opportunity for mobile vision technologies to improve road safety by understanding and monitoring the driver. In this work, we propose a real-time framework for early detection of driver maneuvers. the implications of this study would allow for better behavior prediction, and therefore the development of more efficient advanced driver assistance and warning systems. Cues are extracted from an array of sensors observing the driver (head, hand, and foot), the environment (lane and surrounding vehicles), and the ego-vehicle state (speed, steering angle, etc.). Evaluation is performed on a real-world dataset with overtaking maneuvers, showing promising results. In order to gain better insight into the processes that characterize driver behavior, temporally discriminative cues are studied and visualized.

关键词： active safety driver assistance systems mobile vision applications real-time behavior analysis temporal action recognition

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Clothing Co-Parsing by Joint Image Segmentation and Labeling 27

Clothing Co-Parsing by Joint Image Segmentation and Labeling

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

作者： Yang, Wei Luo, Ping Lin, Liang Sun Yat Sen Univ Guangzhou 510275 Guangdong Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China SYSU CMU Shunde Int Joint Res Inst Shunde Peoples R China

ISBN: (纸本)9781479951178

this paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. We propose a data-driven framework consisting of two phases of inference. the first phase, referred as "image co-segmentation", iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM (ESVM) technique [23]. In the second phase (i.e. "region colabeling"), we construct a multi-image graphical model by taking the segmented regions as vertices, and incorporate several contexts of clothing configuration (e.g., item location and mutual interactions). the joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [30], we construct a dataset called CCP consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89% recognition rate on the Fashionista and the CCP datasets, respectively, which are superior compared with state-of-the-art methods.

关键词： Clothing recognition EM Algorithm Human Parsing Image Understand

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Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders 27

Who Do I Look Like? Determining Parent-Offspring Resemblance...

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

作者： Dehghan, Afshin Ortiz, Enrique G. Villegas, Ruben Shah, Mubarak Univ Cent Florida Ctr Comp Vis Res Orlando FL 32816 USA

ISBN: (纸本)9781479951178

Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks. In this paper, we consider the difficult task of determining parent-offspring resemblance using deep learning to answer the question "Who do I look like?" Although humans can perform this job at a rate higher than chance, it is not clear how they do it [2]. However, recent studies in anthropology [24] have determined which features tend to be the most discriminative. In this study, we aim to not only create an accurate system for resemblance detection, but bridge the gap between studies in anthropology with computer vision techniques. Further, we aim to answer two key questions: 1) Do offspring resemble their parents? and 2) Do offspring resemble one parent more than the other? We propose an algorithm that fuses the features and metrics discovered via gated autoencoders with a discriminative neural network layer that learns the optimal, or what we call genetic, features to delineate parent-offspring relationships. We further analyze the correlation between our automatically detected features and those found in anthropological studies. Meanwhile, our method outperforms the state-of-the-art in kinship verification by 3-10% depending on the relationship using specific (father-son, mother-daughter, etc.) and generic models.

关键词： Deep Learning Face recognition Gated-autoencoder Kinship recognition Parent-Offspring Resemblence Facial recognition Anthropology GATING images Social Networks Parents Machine Learning computer vision descendant jobs Chance Answer Metronidazole

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Randomized Max-Margin Compositions for Visual recognition 27

Randomized Max-Margin Compositions for Visual Recognition

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

作者： Eigenstetter, Angela Takami, Masato Ommer, Bjoern Heidelberg Univ Heidelberg Collaboratory Image Proc & IWR D-69115 Heidelberg Germany Robert Bosch GmbH Corp Res Hildesheim Germany

ISBN: (纸本)9781479951178

A main theme in object detection are currently discriminative part-based models. the powerful model that combines all parts is then typically only feasible for few constituents, which are in turn iteratively trained to make them as strong as possible. We follow the opposite strategy by randomly sampling a large number of instance specific part classifiers. Due to their number, we cannot directly train a powerful classifier to combine all parts. therefore, we randomly group them into fewer, overlapping compositions that are trained using a maximum-margin approach. In contrast to the common rationale of compositional approaches, we do not aim for semantically meaningful ensembles. Rather we seek randomized compositions that are discriminative and generalize over all instances of a category. Our approach not only localizes objects in cluttered scenes, but also explains them by parsing with compositions and their constituent parts. We conducted experiments on PASCAL VOC07, on the VOC10 evaluation server, and on the MITIndoor scene dataset. To the best of our knowledge, our randomized max-margin compositions ((RMC)-C-2) are the currently best performing single class object detector using only HOG features. Moreover, the individual contributions of compositions and their parts are evaluated in separate experiments that demonstrate their potential.

关键词： compositionality mid-level representation object detection part-based models randomized models scene classification visual parsing visual recognition

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