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Visual tracking using attention-modulated disintegration and integration

Visual tracking using attention-modulated disintegration and...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Choi, Jongwon Chang, Hyung Jin Jeong, Jiyeoup Demiris, Yiannis Choi, Jin Young ASRI Dept. of Electrical and Computer Eng. Seoul National University Korea Republic of Dept. of Electrical and Electronic Eng. Imperial College London United Kingdom

ISBN: (纸本)9781467388511

In this paper, we present a novel attention-modulated visual tracking algorithm that decomposes an object into multiple cognitive units, and trains multiple elementary trackers in order to modulate the distribution of attention according to various feature and kernel types. In the integration stage it recombines the units to memorize and recognize the target object effectively. With respect to the elementary trackers, we present a novel attentional feature-based correlation filter (AtCF) that focuses on distinctive attentional features. The effectiveness of the proposed algorithm is validated through experimental comparison with state-of-theart methods on widely-used tracking benchmark datasets.

关键词： Fluorine compounds

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Semi-supervised vocabulary-informed learning

Semi-supervised vocabulary-informed learning

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Fu, Yanwei Sigal, Leonid Disney Research United States

ISBN: (纸本)9781467388511

Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, ability to learn from limited labeled data and ability to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of semi-supervised vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot and open set recognition using a unified framework. Specifically, we propose a maximum margin framework for semantic manifoldbased recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms, ensuring that labeled samples are projected closest to their correct prototypes, in the embedding space, than to others. We show that resulting model shows improvements in supervised, zero-shot, and large open set recognition, with up to 310K class vocabulary on AwA and ImageNet datasets.

关键词： computer vision

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Sample-specific SVM learning for person re-identification

Sample-specific SVM learning for person re-identification

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhang, Ying Li, Baohua Lu, Huchuan Irie, Atshushi Ruan, Xiang Dalian University of Technology Dalian116023 China OMRON Corporation Kusatsu Japan

ISBN: (纸本)9781467388511

Person re-identification addresses the problem of matching people across disjoint camera views and extensive efforts have been made to seek either the robust feature representation or the discriminative matching metrics. However, most existing approaches focus on learning a fixed distance metric for all instance pairs, while ignoring the individuality of each person. In this paper, we formulate the person re-identification problem as an imbalanced classification problem and learn a classifier specifically for each pedestrian such that the matching model is highly tuned to the individual's appearance. To establish correspondence between feature space and classifier space, we propose a Least Square Semi-Coupled Dictionary Learning (LSSCDL) algorithm to learn a pair of dictionaries and a mapping function efficiently. Extensive experiments on a series of challenging databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art approaches, especially on the rank-1 recognition rate.

关键词： pattern recognition

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Information-driven adaptive structured-light scanners

Information-driven adaptive structured-light scanners

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Rosman, Guy Rus, Daniela Fisher, John W. CSAIL Massachusetts Institute of Technology United States

ISBN: (纸本)9781467388511

Sensor planning and active sensing, long studied in robotics, adapt sensor parameters to maximize a utility function while constraining resource expenditures. Here we consider information gain as the utility function. While these concepts are often used to reason about 3D sensors, these are usually treated as a predefined, black-box, component. In this paper we show how the same principles can be used as part of the 3D sensor. We describe the relevant generative model for structured-light 3D scanning and show how adaptive pattern selection can maximize information gain in an open-loop-feedback manner. We then demonstrate how different choices of relevant variable sets (corresponding to the subproblems of locatization and mapping) lead to different criteria for pattern selection and can be computed in an online fashion. We show results for both subproblems with several pattern dictionary choices and demonstrate their usefulness for pose estimation and depth acquisition.

关键词： computer vision

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A hierarchical deep temporal model for group activity recognition

A hierarchical deep temporal model for group activity recogn...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Ibrahim, Mostafa S. Muralidharan, Srikanth Deng, Zhiwei Vahdat, Arash Mori, Greg School of Computing Science Simon Fraser University Burnaby Canada

ISBN: (纸本)9781467388511

In group activity recognition, the temporal dynamics of the whole activity can be inferred based on the dynamics of the individual people representing the activity. We build a deep model to capture these dynamics based on LSTM (long short-term memory) models. To make use of these observations, we present a 2-stage deep temporal model for the group activity recognition problem. In our model, a LSTM model is designed to represent action dynamics of individual people in a sequence and another LSTM model is designed to aggregate person-level information for whole activity understanding. We evaluate our model over two datasets: the Collective Activity Dataset and a new volleyball dataset. Experimental results demonstrate that our proposed model improves group activity recognition performance compared to baseline methods.

关键词： Long short-term memory

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Learning compact binary descriptors with unsupervised deep neural networks

Learning compact binary descriptors with unsupervised deep n...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Lin, Kevin Lu, Jiwen Chen, Chu-Song Zhou, Jie Institute of Information Science Academia Sinica Taipei Taiwan Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9781467388511

In this paper, we propose a new unsupervised deep learning approach called DeepBit to learn compact binary descriptor for efficient visual object matching. Unlike most existing binary descriptors which were designed with random projections or linear hash functions, we develop a deep neural network to learn binary descriptors in an unsupervised manner. We enforce three criterions on binary codes which are learned at the top layer of our network:1) minimal loss quantization, 2) evenly distributed codes and 3) uncorrelated bits. Then, we learn the parameters of the networks with a back-propagation technique. Experimental results on three different visual analysis tasks including image matching, image retrieval, and object recognition clearly demonstrate the effectiveness of the proposed approach.

关键词： pattern recognition

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Compact Bilinear pooling

Compact Bilinear pooling

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Gao, Yang Beijbom, Oscar Zhang, Ning Darrell, Trevor EECS UC Berkeley United States Snapchat Inc. United States

ISBN: (纸本)9781467388511

Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. However, bilinear features are high dimensional, typically on the order of hundreds of thousands to a few million, which makes them impractical for subsequent analysis. We propose two compact bilinear representations with the same discriminative power as the full bilinear representation but with only a few thousand dimensions. Our compact representations allow back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system. The compact bilinear representations are derived through a novel kernelized analysis of bilinear pooling which provide insights into the discriminative power of bilinear pooling, and a platform for further research in compact pooling methods. Experimentation illustrate the utility of the proposed representations for image classification and few-shot learning across several datasets.

关键词： Face recognition

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Recognizing car fluents from video

Recognizing car fluents from video

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Li, Bo Wu, Tianfu Xiong, Caiming Zhu, Song-Chun Beijing Lab of Intelligent Information Technology Beijing Institute of Technology China Department of Statistics University of California Los Angeles United States Metamind Inc. United States

ISBN: (纸本)9781467388511

Physical fluents, a term originally used by Newton [40], refers to time-varying object states in dynamic scenes. In this paper, we are interested in inferring the fluents of vehicles from video. For example, a door (hood, trunk) is open or closed through various actions, light is blinking to turn. Recognizing these fluents has broad applications, yet have received scant attention in the computer vision literature. Car fluent recognition entails a unified framework for car detection, car part localization and part status recognition, which is made difficult by large structural and appearance variations, low resolutions and occlusions. This paper learns a spatial-temporal And-Or hierarchical model to represent car fluents. The learning of this model is formulated under the latent structural SVM framework. Since there are no publicly related dataset, we collect and annotate a car fluent dataset consisting of car videos with diverse fluents. In experiments, the proposed method outperforms several highly related baseline methods in terms of car fluent recognition and car part localization.

关键词： pattern recognition

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Dynamic image networks for action recognition

Dynamic image networks for action recognition

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Bilen, Hakan Fernando, Basura Gavves, Efstratios Vedaldi, Andrea Gould, Stephen University of Oxford United Kingdom Australian National University Australia QUVA Lab. University of Amsterdam Netherlands

ISBN: (纸本)9781467388511

We introduce the concept of dynamic image, a novel compact representation of videos useful for video analysis especially when convolutional neural networks (CNNs) are used. The dynamic image is based on the rank pooling concept and is obtained through the parameters of a ranking machine that encodes the temporal evolution of the frames of the video. Dynamic images are obtained by directly applying rank pooling on the raw image pixels of a video producing a single RGB image per video. This idea is simple but powerful as it enables the use of existing CNN models directly on video data with fine-tuning. We present an efficient and effective approximate rank pooling operator, speeding it up orders of magnitude compared to rank pooling. Our new approximate rank pooling CNN layer allows us to generalize dynamic images to dynamic feature maps and we demonstrate the power of our new representations on standard benchmarks in action recognition achieving state-of-the-art performance.

关键词： computer vision

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Context-aware Gaussian fields for non-rigid point set registration

Context-aware Gaussian fields for non-rigid point set regist...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wang, Gang Wang, Zhicheng Chen, Yufei Zhou, Qiangqiang Zhao, Weidong CAD Research Center College of Electronics and Information Engineering Tongji University Shanghai201804 China

ISBN: (纸本)9781467388511

Point set registration (PSR) is a fundamental problem in computer vision and pattern recognition, and it has been successfully applied to many applications. Although widely used, existing PSR methods cannot align point sets robustly under degradations, such as deformation, noise, occlusion, outlier, rotation, and multi-view changes. This paper proposes context-aware Gaussian fields (CA-LapGF) for nonrigid PSR subject to global rigid and local non-rigid geometric constraints, where a laplacian regularized term is added to preserve the intrinsic geometry of the transformed set. CA-LapGF uses a robust objective function and the quasi-Newton algorithm to estimate the likely correspondences, and the non-rigid transformation parameters between two point sets iteratively. The CA-LapGF can estimate non-rigid transformations, which are mapped to reproducing kernel Hilbert spaces, accurately and robustly in the presence of degradations. Experimental results on synthetic and real images reveal that how CA-LapGF outperforms state-of-the-art algorithms for non-rigid PSR.

关键词： pattern recognition

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