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Multi-target Tracking by Rank-1 Tensor Approximation

Multi-target Tracking by Rank-1 Tensor Approximation

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Shi, Xinchu Ling, Haibin Xing, Junliang Hu, Weiming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9780769549897

In this paper we formulate multi-target tracking (MTT) as a rank-1 tensor approximation problem and propose an l(1) norm tensor power iteration solution. In particular, a high order tensor is constructed based on trajectories in the time window, with each tensor element as the affinity of the corresponding trajectory candidate. the local assignment variables are the l(1) normalized vectors, which are used to approximate the rank-1 tensor. Our approach provides a flexible and effective formulation where both pairwise and high-order association energies can be used expediently. We also show the close relation between our formulation and the multi-dimensional assignment (MDA) model. To solve the optimization in the rank-1 tensor approximation, we propose an algorithm that iteratively powers the intermediate solution followed by an l(1) normalization. Aside from effectively capturing high-order motion information, the proposed solver runs efficiently with proved convergence. the experimental validations are conducted on two challenging datasets and our method demonstrates promising performances on both.

关键词： approximation theory computer vision human computer interaction iterative methods target tracking tensors vectors

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Cumulative Attribute Space for Age and Crowd Density Estimation

Cumulative Attribute Space for Age and Crowd Density Estimat...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Chen, Ke Gong, Shaogang Xiang, Tao Loy, Chen Change Univ London London E1 4NS England Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769549897

A number of computer vision problems such as human age estimation, crowd density estimation and body/face pose (view angle) estimation can be formulated as a regression problem by learning a mapping function between a high dimensional vector-formed feature input and a scalar-valued output. Such a learning problem is made difficult due to sparse and imbalanced training data and large feature variations caused by both uncertain viewing conditions and intrinsic ambiguities between observable visual features and the scalar values to be estimated. Encouraged by the recent success in using attributes for solving classification problems with sparse training data, this paper introduces a novel cumulative attribute concept for learning a regression model when only sparse and imbalanced data are available. More precisely, low-level visual features extracted from sparse and imbalanced image samples are mapped onto a cumulative attribute space where each dimension has clearly defined semantic interpretation (a label) that captures how the scalar output value (e. g. age, people count) changes continuously and cumulatively. Extensive experiments show that our cumulative attribute framework gains notable advantage on accuracy for both age estimation and crowd counting when compared against conventional regression models, especially when the labelled training data is sparse with imbalanced sampling.

关键词： computer vision feature extraction image classification learning (artificial intelligence) pose estimation regression analysis

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Single-Pedestrian Detection aided by Multi-pedestrian Detection

Single-Pedestrian Detection aided by Multi-pedestrian Detect...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Ouyang, Wanli Wang, Xiaogang Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pat Rec Beijing Peoples R China

ISBN: (纸本)9780769549897

In this paper, we address the challenging problem of detecting pedestrians who appear in groups and have interaction. A new approach is proposed for single-pedestrian detection aided by multi-pedestrian detection. A mixture model of multi-pedestrian detectors is designed to capture the unique visual cues which are formed by nearby multiple pedestrians but cannot be captured by single-pedestrian detectors. A probabilistic framework is proposed to model the relationship between the configurations estimated by single-and multi-pedestrian detectors, and to refine the single-pedestrian detection result with multi-pedestrian detection. It can integrate with any single-pedestrian detector without significantly increasing the computation load. 15 state-of-the-art single-pedestrian detection approaches are investigated on three widely used public datasets: Caltech, TUD-Brussels and Eth. Experimental results show that our framework significantly improves all these approaches. the average improvement is 9% on the Caltech-Test dataset, 11% on the TUD-Brussels dataset and 17% on the Eth dataset in terms of average miss rate. the lowest average miss rate is reduced from 48% to 43% on the Caltech-Test dataset, from 55% to 50% on the TUD-Brussels dataset and from 51% to 41% on the Eth dataset.

关键词： object detection pedestrians probability video surveillance

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Weakly-Supervised Dual Clustering for Image Semantic Segmentation

Weakly-Supervised Dual Clustering for Image Semantic Segment...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Liu, Yang Liu, Jing Li, Zechao Tang, Jinhui Lu, Hanqing Chinese Acad Sci Inst Automat NLPR Beijing 100190 Peoples R China Nanjing Univ Sci & Tech Sch Comp Sci Beijing 210044 Peoples R China

ISBN: (纸本)9780769549897

In this paper, we propose a novel Weakly-Supervised Dual Clustering (WSDC) approach for image semantic segmentation with image-level labels, i.e., collaboratively performing image segmentation and tag alignment with those regions. the proposed approach is motivated from the observation that superpixels belonging to an object class usually exist across multiple images and hence can be gathered via the idea of clustering. In WSDC, spectral clustering is adopted to cluster the superpixels obtained from a set of over-segmented images. At the same time, a linear transformation between features and labels as a kind of discriminative clustering is learned to select the discriminative features among different classes. the both clustering outputs should be consistent as much as possible. Besides, weakly-supervised constraints from image-level labels are imposed to restrict the labeling of superpixels. Finally, the non-convex and non-smooth objective function are efficiently optimized using an iterative CCCP procedure. Extensive experiments conducted on MSRC and LabelMe datasets demonstrate the encouraging performance of our method in comparison with some state-of-the-arts.

关键词： image segmentation pattern clustering

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Fast Trust Region for Segmentation

Fast Trust Region for Segmentation

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Gorelick, Lena Schmidt, Frank R. Boykov, Yuri Univ Western Ontario Comp Vis Grp London ON N6A 3K7 Canada Univ Freiburg BIOSS Ctr Biol Signalling Studies Freiburg Germany

ISBN: (纸本)9780769549897

Trust region is a well-known general iterative approach to optimization which offers many advantages over standard gradient descent techniques. In particular, it allows more accurate nonlinear approximation models. In each iteration this approach computes a global optimum of a suitable approximation model within a fixed radius around the current solution, a.k.a. trust region. In general, this approach can be used only when some efficient constrained optimization algorithm is available for the selected nonlinear (more accurate) approximation model. In this paper we propose a Fast Trust Region (FTR) approach for optimization of segmentation energies with nonlinear regional terms, which are known to be challenging for existing algorithms. these energies include, but are not limited to, KL divergence and Bhattacharyya distance between the observed and the target appearance distributions, volume constraint on segment size, and shape prior constraint in a form of.. 2 distance from target shape moments. Our method is 1-2 orders of magnitude faster than the existing state-of-the-art methods while converging to comparable or better solutions.

关键词： computer vision convergence image segmentation iterative methods optimisation

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Fast Energy Minimization using Learned State Filters

Fast Energy Minimization using Learned State Filters

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Guillaumin, Matthieu Van Gool, Luc Ferrari, Vittorio Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium Univ Edinburgh Edinburgh EH8 9YL Midlothian Scotland

ISBN: (纸本)9780769549897

Pairwise discrete energies defined over graphs are ubiquitous in computer vision. Many algorithms have been proposed to minimize such energies, often concentrating on sparse graph topologies or specialized classes of pairwise potentials. However, when the graph is fully connected and the pairwise potentials are arbitrary, the complexity of even approximate minimization algorithms such as TRW-S grows quadratically both in the number of nodes and in the number of states a node can take. Moreover, recent applications are using more and more computationally expensive pairwise potentials. these factors make it very hard to employ fully connected models. In this paper we propose a novel, generic algorithm to approximately minimize any discrete pairwise energy function. Our method exploits tractable sub-energies to filter the domain of the function. the parameters of the filter are learnt from instances of the same class of energies with good candidate solutions. Compared to existing methods, it efficiently handles fully connected graphs, with many states per node, and arbitrary pairwise potentials, which might be expensive to compute. We demonstrate experimentally on two applications that our algorithm is much more efficient than other generic minimization algorithms such as TRW-S, while returning essentially identical solutions.

关键词： approximation theory computer vision filtering theory graph theory minimisation

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Image Tag Completion via Image-Specific and Tag-Specific Linear Sparse Reconstructions

Image Tag Completion via Image-Specific and Tag-Specific Lin...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Lin, Zijia Ding, Guiguang Hu, Mingqing Wang, Jianmin Ye, Xiaojun Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9780769549897

though widely utilized for facilitating image management, user-provided image tags are usually incomplete and insufficient to describe the whole semantic content of corresponding images, resulting in performance degradations in tag-dependent applications and thus necessitating effective tag completion methods. In this paper, we propose a novel scheme denoted as LSR for automatic image tag completion via image-specific and tag-specific Linear Sparse Reconstructions. Given an incomplete initial tagging matrix with each row representing an image and each column representing a tag, LSR optimally reconstructs each image (i.e. row) and each tag (i.e. column) with remaining ones under constraints of sparsity, considering image-image similarity, image-tag association and tag-tag concurrence. then both image-specific and tag-specific reconstruction values are normalized and merged for selecting missing related tags. Extensive experiments conducted on both benchmark dataset and web images well demonstrate the effectiveness of the proposed LSR.

关键词： image reconstruction matrix algebra

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Object Tracking by Occlusion Detection via Structured Sparse Learning

Object Tracking by Occlusion Detection via Structured Sparse...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Tianzhu Ghanem, Bernard Xu, Changsheng Ahuja, Narendra Adv Digital Sci Ctr Illinois Singapore Singapore King Abdulaziz Univ Sci Technol Thuwal Saudi Arabia Chinese Acad Sci Inst Automat CSIDM Beijing Peoples R China Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9780769549903

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. they assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object's track. this is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. this propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art.

关键词： computer vision image representation learning (artificial intelligence) object detection object tracking

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Video Editing with Temporal, Spatial and Appearance Consistency

Video Editing with Temporal, Spatial and Appearance Consiste...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Guo, Xiaojie Cao, Xiaochun Chen, Xiaowu Ma, Yi Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China Chinese Acad Sci IIE State Key Lab Informat Secur Beijing 100093 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Microsoft Res Asia Visual Comp Grp Beijing 100080 Peoples R China

ISBN: (纸本)9780769549897

Given an area of interest in a video sequence, one may want to manipulate or edit the area, e.g. remove occlusions from or replace with an advertisement on it. Such a task involves three main challenges including temporal consistency, spatial pose, and visual realism. the proposed method effectively seeks an optimal solution to simultaneously deal with temporal alignment, pose rectification, as well as precise recovery of the occlusion. To make our method applicable to long video sequences, we propose a batch alignment method for automatically aligning and rectifying a small number of initial frames, and then show how to align the remaining frames incrementally to the aligned base images. From the error residual of the robust alignment process, we automatically construct a trimap of the region for each frame, which is used as the input to alpha matting methods to extract the occluding foreground. Experimental results on both simulated and real data demonstrate the accurate and robust performance of our method.

关键词： hidden feature removal image sequences video signal processing

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Binary Code Ranking with Weighted Hamming Distance

Binary Code Ranking with Weighted Hamming Distance

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Lei Zhang, Yongdong Tang, Jinhui Lu, Ke Tian, Qi Chinese Acad Sci Inst Comp Technol 6 Kexueyuan South Rd Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China Univ Texas San Antonio San Antonio TX USA

ISBN: (纸本)9780769549897

Binary hashing has been widely used for efficient similarity search due to its query and storage efficiency. In most existing binary hashing methods, the high-dimensional data are embedded into Hamming space and the distance or similarity of two points are approximated by the Hamming distance between their binary codes. the Hamming distance calculation is efficient, however, in practice, there are often lots of results sharing the same Hamming distance to a query, which makes this distance measure ambiguous and poses a critical issue for similarity search where ranking is important. In this paper, we propose a weighted Hamming distance ranking algorithm (WhRank) to rank the binary codes of hashing methods. By assigning different bit-level weights to different hash bits, the returned binary codes are ranked at a finer-grained binary code level. We give an algorithm to learn the data-adaptive and query-sensitive weight for each hash bit. Evaluations on two large-scale image data sets demonstrate the efficacy of our weighted Hamming distance for binary code ranking.

关键词： Hamming codes approximation theory binary codes cryptography

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