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L₀ Regularized Stationary Time Estimation for Crowd Group Analysis 27

L0 Regularized Stationary Time Estimation ...

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Yi, Shuai Wang, Xiaogang Lu, Cewu Jia, Jiaya Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

We tackle stationary crowd analysis in this paper, which is similarly important as modeling mobile groups in crowd scenes and finds many applications in surveillance. Our key contribution is to propose a robust algorithm of estimating how long a foreground pixel becomes stationary. It is much more challenging than only subtracting background because failure at a single frame due to local movement of objects, lighting variation, and occlusion could lead to large errors on stationary time estimation. To accomplish decent results, sparse constraints along spatial and temporal dimensions are jointly added by mixed partials to shape a 3D stationary time map. It is formulated as a L-0 optimization problem. Besides background subtraction, it distinguishes among different foreground objects, which are close or overlapped in the spatio-temporal space by using a locally shared foreground codebook. the proposed technologies are used to detect four types of stationary group activities and analyze crowd scene structures. We provide the first public benchmark dataset(1) for stationary time estimation and stationary group analysis.

关键词： pattern recognition

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Fully Convolutional Network for Head Detection with Depth Images 24

Fully Convolutional Network for Head Detection with Depth Im...

引用

24th International conference on pattern recognition (ICPR)

作者： Ballotta, Diego Borghi, Guido Vezzani, Roberto Cucchiara, Rita Univ Modena & Reggio Emilia DIEF Via P Vivarelli 10 I-41125 Modena Italy

ISBN: (纸本)9781538637883

Head detection and localization are one of the most investigated and demanding tasks of the computer vision community. these are also a key element for many disciplines, like Human computer Interaction, Human Behavior Understanding, Face Analysis and Video Surveillance. In last decades, many efforts have been conducted to develop accurate and reliable head or face detectors on standard RGB images, but only few solutions concern other types of images, such as depth maps. In this paper, we propose a novel method for head detection on depth images, based on a deep learning approach. In particular, the presented system overcomes the classic sliding-window approach, that is often the main computational bottleneck of many object detectors, through a Fully Convolutional Network. Two public datasets, namely Pandora and Watch-n-Patch, are exploited to train and test the proposed network. Experimental results confirm the effectiveness of the method, that is able to exceed all the state-of-art works based on depth images and to run with real time performance.

关键词： Task analysis Face Detectors Real-time systems Training computer vision

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Face recognition System with Feature Fusion for Rehabilitation Robots in Healthcare 7

Face Recognition System with Feature Fusion for Rehabilitati...

引用

7th International conference on computer and Communications, ICCC 2021

作者： Bhutto, Amna Xu, Linsen Sattar, Ammara Chinese Academy of Sciences Hefei Institute of Physical Science Hefei230031 China University of Science and Technology of China Hefei230026 China Hohai University Mechatronic Engineering China Harbin Engineering University Harbin China

ISBN: (纸本)9781665409506

Face recognition is a research problem across multiple disciplines such as computer vision, pattern recognition, artificial intelligence, psychology, healthcare etc. At present, the rehabilitation centers in hospitals are dependent on the nursing staff with no implementation of rehabilitation automation. In this study of face detection, a facial expression recognition system was developed which can be useful for robots of rehabilitation nursing beds. this article mainly researches two aspects: face image feature extraction and classifier design. Since many low-resolution face images are often encountered in actual environments (such as surveillance), if you use traditional methods to directly extract image features, the results are often not satisfactory. For the influence of face recognition results, this paper proposes a feature fusion extraction method based on KNN with LDA(Fisherfaces) and PCA (eigenfaces). First, the original image (low resolution) is decomposed into three different resolution images, and then the three images are used as the basis for the position to extract facial features, then LDA and PCA are used to perform preliminary analysis to obtain reduced dimensional reduction features. Finally use the K-nearest neighbor classifier to perform classification prediction. Study compares KNN-LDA with KNN-PCA with different distances of KNN algorithm. © 2021 IEEE.

关键词： Discriminant analysis

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Video Compression for Object Detection Algorithms 24

Video Compression for Object Detection Algorithms

引用

24th International conference on pattern recognition (ICPR)

作者： Galteri, Leonardo Bertini, Marco Seidenari, Lorenzo Del Bimbo, Alberto Univ Firenze Media Integrat & Commun Ctr Viale Morgagni 65 I-50134 Florence Italy

ISBN: (纸本)9781538637883

Video compression algorithms have been designed aiming at pleasing human viewers, and are driven by video quality metrics that are designed to account for the capabilities of the human visual system. However, thanks to the advances in computer vision systems more and more videos are going to be watched by algorithms, e.g. implementing video surveillance systems or performing automatic video tagging. this paper describes an adaptive video coding approach for computer vision-based systems. We show how to control the quality of video compression so that automatic object detectors can still process the resulting video, improving their detection performance, by preserving the elements of the scene that are more likely to contain meaningful content. Our approach is based on computation of saliency maps exploiting a fast objectness measure. the computational efficiency of this approach makes it usable in a real-time video coding pipeline. Experiments show that our technique outperforms standard H.265 in speed and coding efficiency, and can be applied to different types of video domains, from surveillance to web videos.

关键词： Image compression

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Binarized Mode Seeking for Scalable Visual pattern Discovery 30

Binarized Mode Seeking for Scalable Visual Pattern Discovery

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30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Wei Cao, Xiaochun Wang, Rui Guo, Yuanfang Chen, Zhineng Chinese Acad Sci Inst Informat Engn SKLOIS Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781538604571

this paper studies visual pattern discovery in large-scale image collections via binarized mode seeking, where images can only be represented as binary codes for efficient storage and computation. We address this problem from the perspective of binary space mode seeking. First, a binary mean shift (bMS) is proposed to discover frequent patterns via mode seeking directly in binary space. the binomial-based kernel and binary constraint are introduced for binarized analysis. Second, we further extend bMS to a more general form, namely contrastive binary mean shift (cbMS), which maximizes the contrastive density in binary space, for finding informative patterns that are both frequent and discriminative for the dataset. With the binarized algorithm and optimization, our methods demonstrate significant computation (50x) and storage (32x) improvement compared to standard techniques operating in Euclidean space, while the performance does not largely degenerate. Furthermore, cbMS discovers more informative patterns by suppressing low discriminative modes. We evaluate our methods on both annotated ILSVRC (1M images) and un-annotated blind Flickr (10M images) datasets with million scale images, which demonstrates both the scalability and effectiveness of our algorithms for discovering frequent and informative patterns in large scale collection.

关键词： Binary Scalability images binary code Dataset Euclidean space spatial mode image samples Visualization

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100+Times FasterWeighted Median Filter (WMF) 27

100+Times FasterWeighted Median Filter (WMF)

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Qi Xu, Li Jia, Jiaya Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Lenovo R&T Image & Visual Comp Lab Hong Kong Peoples R China

ISBN: (纸本)9781479951178

Weighted median, in the form of either solver or filter, has been employed in a wide range of computer vision solutions for its beneficial properties in sparsity representation. But it is hard to be accelerated due to the spatially varying weight and the median property. We propose a few efficient schemes to reduce computation complexity from O(r(2)) to O(r) where r is the kernel size. Our contribution is on a new joint-histogram representation, median tracking, and a new data structure that enables fast data access. the effectiveness of these schemes is demonstrated on optical flow estimation, stereo matching, structure-texture separation, image filtering, to name a few. the running time is largely shortened from several minutes to less than 1 second. the source code is provided in the project website.

关键词： acceleration edge-preserving filtering fast image filter image filtering joint-histogram median median tracking necklace table weighted median filter

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Learning Mid-level Filters for Person Re-identification 27

Learning Mid-level Filters for Person Re-identification

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Rui Ouyang, Wanli Wang, Xiaogang Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification. It is well motivated by our study on what are good filters for person re-identification. Our mid-level filters are discriminatively learned for identifying specific visual patterns and distinguishing persons, and have good cross-view invariance. First, local patches are qualitatively measured and classified with their discriminative power. Discriminative and representative patches are collected for filter learning. Second, patch clusters with coherent appearance are obtained by pruning hierarchical clustering trees, and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative. third, filter responses are integrated with patch matching scores in RankSVM training. the effectiveness of our approach is validated on the VIPeR dataset and the CUHK01 dataset. the learned mid-level features are complementary to existing handcrafted low-level features, and improve the best Rank-1 matching rate on the VIPeR dataset by 14%.

关键词： Mid-level filter person re-identification

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Online Content-aware Video Condensation

Online Content-aware Video Condensation

引用

IEEE conference on computer vision and pattern recognition (CVPR)

作者： Feng, Shikun Lei, Zhen Yi, Dong Li, Stan Z. Chinese Acad Sci Ctr Biometr & Secur Res Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9781467312288

Explosive growth of surveillance video data presents formidable challenges to its browsing, retrieval and storage. Video synopsis, an innovation proposed by Peleg and his colleagues, is aimed for fast browsing by shortening the video into a synopsis while keeping activities in video captured by a camera. However, the current techniques are offline methods requiring that all the video data be ready for the processing, and are expensive in time and space. In this paper, we propose an online and efficient solution, and its supporting algorithms to overcome the problems. the method adopts an online content-aware approach in a stepwise manner, hence applicable to endless video, with less computational cost. Moreover, we propose a novel tracking method, called sticky tracking, to achieve high-quality visualization. the system can achieve a faster-than-real-time speed with a multi-core CPU implementation. the advantages are demonstrated by extensive experiments with a wide variety of videos. the proposed solution and algorithms could be integrated with surveillance cameras, and impact the way that surveillance videos are recorded.

关键词： Digital storage

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Unnatural L₀ Sparse Representation for Natural Image Deblurring

Unnatural L0 Sparse Representation for Nat...

引用

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

作者： Xu, Li Zheng, Shicheng Jia, Jiaya Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769549897

We show in this paper that the success of previous maximum a posterior (MAP) based blur removal methods partly stems from their respective intermediate steps, which implicitly or explicitly create an unnatural representation containing salient image structures. We propose a generalized and mathematically sound L-0 sparse expression, together with a new effective method, for motion deblurring. Our system does not require extra filtering during optimization and demonstrates fast energy decreasing, making a small number of iterations enough for convergence. It also provides a unified framework for both uniform and non-uniform motion deblurring. We extensively validate our method and show comparison with other approaches with respect to convergence speed, running time, and result quality.

关键词： convergence of numerical methods image motion analysis image representation image restoration iterative methods optimisation sparse matrices

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Active Flattening of Curved Document Images via Two Structured Beams 27

Active Flattening of Curved Document Images via Two Structur...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Meng, Gaofeng Wang, Ying Qu, Shenquan Xiang, Shiming Pan, Chunhong Chinese Acad Sceiences CASIA NLPR Inst Automat Beijing 100190 Peoples R China

ISBN: (纸本)9781479951178

Document images captured by a digital camera often suffer from serious geometric distortions. In this paper, we propose an active method to correct geometric distortions in a camera-captured document image. Unlike many passive rectification methods that rely on text-lines or features extracted from images, our method uses two structured beams illuminating upon the document page to recover two spatial curves. A developable surface is then interpolated to the curves by finding the correspondence between them. the developable surface is finally flattened onto a plane by solving a system of ordinary differential equations. Our method is a content independent approach and can restore a corrected document image of high accuracy with undistorted contents. Experimental results on a variety of real-captured document images demonstrate the effectiveness and efficiency of the proposed method.

关键词： document image processing geometric rectification structured beams developable surface interpolation

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