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共 2181 条记录，以下是311-320 订阅

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A Color Model Based Fire Flame Detection System 7th

A Color Model Based Fire Flame Detection System

引用

7th chinese conference on pattern recognition (CCPR)

作者： Lu, Qing Yu, Jun Wang, Zengfu Univ Sci & Technol China Dept Automat Hefei 230026 Anhui Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei 230026 Anhui Peoples R China

ISBN: (纸本)9789811030024;9789811030017

Fire flame detection using color information is an important problem for public security and has many applications in computer vision and other domains. the color model based method used for fire flame detection has many advantages over conventional methods, such as simple, feasible and understandable. In order to improve the performance of fire flame detection based on video, we propose an effective color model based method for fire flame detection and build a corresponding fire flame detection system. Firstly, candidate fire flame regions are detected using the chromatic and dynamic measurements. Secondly, the fire flame regions are determined based on the area of the candidate regions. Finally, the fire flame detection system will give an alarm voice when the number of successive fire frames surpasses threshold. Experimental results show the effectiveness of our system on various fire-detection tasks in real-world environments.

关键词： computer vision Color model Fire flame detection system Chromatic and dynamic measurements

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Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks 30

Modeling Temporal Dynamics and Spatial Configurations of Act...

引用

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

作者： Wang, Hongsong Wang, Liang Chinese Acad Sci CASIA NLPR CRIPAC Beijing Peoples R China Chinese Acad Sci Inst Automat CEBSIT Beijing Peoples R China UCAS Beijing Peoples R China

ISBN: (纸本)9781538604571

Recently, skeleton based action recognition gains more popularity due to cost-effective depth sensors coupled with real-time skeleton estimation algorithms. Traditional approaches based on handcrafted features are limited to represent the complexity of motion patterns. Recent methods that use Recurrent Neural Networks (RNN) to handle raw skeletons only focus on the contextual dependency in the temporal domain and neglect the spatial configurations of articulated skeletons. In this paper, we propose a novel two-stream RNN architecture to model both temporal dynamics and spatial configurations for skeleton based action recognition. We explore two different structures for the temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed according to human body kinematics. We also propose two effective methods to model the spatial structure by converting the spatial graph into a sequence of joints. To improve generalization of our model, we further exploit 3D transformation based data augmentation techniques including rotation and scaling transformation to transform the 3D coordinates of skeletons during training. Experiments on 3D action recognition benchmark datasets show that our method brings a considerable improvement for a variety of actions, i.e., generic actions, interaction activities and gestures.

关键词： recurrent neural nets Skeletons bone structure Action Gestures

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the Analysis of Facial Beauty: An Emerging Area of Research in pattern Analysis

The Analysis of Facial Beauty: An Emerging Area of Research ...

引用

7th International conference on Image Analysis and recognition

作者： Bottino, Andrea Laurentini, Aldo Politecn Torino Dipartimento Automat & Informat I-10129 Turin Italy

ISBN: (纸本)9783642137716

Much research presented recently supports the idea that the human perception of attractiveness is data-driven and largely irrespective of the perceiver. this suggests using pattern analysis techniques for beauty analysis. Several scientific papers on this subject are appearing in image processing. computer vision and pattern analysis contexts, or use techniques of these areas. In this paper, we will survey the recent studies on automatic analysis of facial beauty, and discuss research lines and practical applications.

关键词： Face image analysis facial landmarks attractiveness

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Relative Pose Estimation in Binocular vision for a Planar Scene using Inter-Image Homographies 7

Relative Pose Estimation in Binocular Vision for a Planar Sc...

引用

7th International conference on pattern recognition Applications and Methods (ICPRAM)

作者： Ornhag, Marcus Valtonen Heyden, Anders Lund Univ Ctr Math Sci Lund Sweden

ISBN: (纸本)9789897582769

In this paper we consider a mobile platform with two cameras directed towards the floor mounted the same distance from the ground, assuming planar motion and constant internal parameters. Earlier work related to this specific problem geometry has been carried out for monocular systems, and the main contribution of this paper is the generalization to a binocular system and the recovery of the relative translation and orientation between the cameras. the method is based on previous work on monocular systems, using sequences of inter-image homographies. Experiments are conducted using synthetic data, and the results demonstrate a robust method for determining the relative parameters.

关键词： Relative Pose Estimation SLAM Visual Odometry Binocular vision Planar Motion Homography

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Detecting and tracking distant objects at night based on human visual system

Detecting and tracking distant objects at night based on hum...

引用

7th Asian conference on computer vision

作者： Huang, KQ Wang, LS Tan, TN Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat Beijing 100080 Peoples R China

ISBN: (纸本)3540312447

Moving object detection is a challenging task for night security because of bad video quality. In this paper, we propose a robust real time objects detection method for night visual surveillance based on human visual system. By measuring contrast information variation in multiple successive frames, a spatio-temporal contrast change image (CCI) is formed. then the multi-frame correspondence technology is employed to robustly extract salient motions or moving objects from CCI. Since CCI is a statistical measurement of variation based on human visual system, the proposed method is effective at night and better than traditional detection methods. Experiments on real scene show that the method based on contrast feature is effective for night object detection and tracking, our approach is also robust to camera scale variation as well as low computation cost.

关键词： Object recognition

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See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-based Person Re-identification 30

See the Forest for the Trees: Joint Spatial and Temporal Rec...

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

作者： Zhou, Zhen Huang, Yan Wang, Wei Wang, Liang Tan, Tieniu Natl Lab Pattern Recognit NLPR Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Ctr Excellence Brain Sci & Intelligence Technol C Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China

ISBN: (纸本)9781538604571

Surveillance cameras have been widely used in different scenes. Accordingly, a demanding need is to recognize a person under different cameras, which is called person re-identification. this topic has gained increasing interests in computer vision recently. However, less attention has been paid to video-based approaches, compared with image-based ones. Two steps are usually involved in previous approaches, namely feature learning and metric learning. But most of the existing approaches only focus on either feature learning or metric learning. Meanwhile, many of them do not take full use of the temporal and spatial information. In this paper, we concentrate on video-based person re-identification and build an end-to-end deep neural network architecture to jointly learn features and metrics. the proposed method can automatically pick out the most discriminative frames in a given video by a temporal attention model. Moreover, it integrates the surrounding information at each location by a spatial recurrent model when measuring the similarity with another pedestrian video. that is, our method handles spatial and temporal information simultaneously in a unified manner. the carefully designed experiments on three public datasets show the effectiveness of each component of the proposed deep network, performing better in comparison with the state-of-the-art methods.

关键词： Measurement Image sequences Feature extraction computer architecture computer vision Recurrent neural networks

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Human vs. computer in scene and object recognition 27

Human vs. computer in scene and object recognition

引用

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

作者： Borji, Ali Itti, Laurent Univ So Calif Dept Comp Sci Los Angeles CA 90089 USA Univ So Calif Dept Psychol Los Angeles CA 90089 USA Univ So Calif Neurosci Grad Program Los Angeles CA 90089 USA

ISBN: (纸本)9781479951178

Several decades of research in computer and primate vision have resulted in many models (some specialized for one problem, others more general) and invaluable experimental data. Here, to help focus research efforts onto the hardest unsolved problems, and bridge computer and human vision, we define a battery of 5 tests that measure the gap between human and machine performances in several dimensions (generalization across scene categories, generalization from images to edge maps and line drawings, invariance to rotation and scaling, local/global information with jumbled images, and object recognition performance). We measure model accuracy and the correlation between model and human error patterns. Experimenting over 7 datasets, where human data is available, and gauging 14 well-established models, we find that none fully resembles humans in all aspects, and we learn from each test which models and features are more promising in approaching humans in the tested dimension. Across all tests, we find that models based on local edge histograms consistently resemble humans more, while several scene statistics or "gist" models do perform well with both scenes and objects. While computer vision has long been inspired by human vision, we believe systematic efforts, such as this, will help better identify shortcomings of models and find new paths forward.

关键词： computer vision human vision jumbled images line drawings object recognition scene recognition

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Brain-inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform

Brain-inspired Classroom Occupancy Monitoring on a Low-Power...

引用

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

作者： Conti, Francesco Pullini, Antonio Benini, Luca Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy Swiss Fed Inst Technol Integrated Syst Lab Zurich Switzerland

ISBN: (纸本)9781479943098

Brain-inspired computer vision (BICV) has evolved rapidly in recent years and it is now competitive with traditional CV approaches. However, most of BICV algorithms have been developed on high power-and-performance platforms (e.g. workstations) or special purpose hardware. We propose two different algorithms for counting people in a classroom, both based on Convolutional Neural Networks (CNNs), a state-of-art deep learning model that is inspired on the structure of the human visual cortex. Furthermore, we provide a standalone parallel C library that implements CNNs and use it to deploy our algorithms on the embedded mobile ARM big. LITTLE-based Odroid-XU platform. Our performance and power measurements show that neuromorphic vision is feasible on off-the-shelf embedded mobile platforms, and we show that it can reach very good energy efficiency for non-time-critical tasks such as people counting.

关键词： brain-inspired computer vision classroom occupancy convolutional neural networks odroid people counting

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Deep Mixture of Linear Inverse Regressions Applied to Head-Pose Estimation 30

Deep Mixture of Linear Inverse Regressions Applied to Head-P...

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

作者： Lathuiliere, Stephane Juge, Remi Mesejo, Pablo Munoz-Salinas, Rafael Horaud, Radu Inria Grenoble Rhone Alpes Montbonnot St Martin France Univ Cordoba Comp & Numer Anal Dept Cordoba Spain

ISBN: (纸本)9781538604571

Convolutional Neural Networks (ConvNets) have become the state-of-the-art for many classification and regression problems in computer vision. When it comes to regression, approaches such as measuring the Euclidean distance of target and predictions are often employed as output layer. In this paper, we propose the coupling of a Gaussian mixture of linear inverse regressions with a ConvNet, and we describe the methodological foundations and the associated algorithm to jointly train the deep network and the regression function. We test our model on the headpose estimation problem. In this particular problem, we show that inverse regression outperforms regression models currently used by state-of-the-art computer vision methods. Our method does not require the incorporation of additional data, as it is often proposed in the literature, thus it is able to work well on relatively small training datasets. Finally, it outperforms state-of-the-art methods in head-pose estimation using a widely used head-pose dataset. To the best of our knowledge, we are the first to incorporate inverse regression into deep learning for computer vision applications.

关键词： images computer vision Inverse regression Regression (Psychology) Dataset Euclidean distance

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Learning invariant feature toward recognition modeling biology vision

Learning invariant feature toward recognition modeling biolo...

引用

7th International conference on Signal Processing

作者： Zou, Q Luo, SW Beijing Jiaotong Univ Dept Comp Sci Beijing 100044 Peoples R China

ISBN: (纸本)7121002159

Generalizing object recognition to be invariant to geometric transformation is a traditional challenge in vision. Inspired by information, processing mechanism in biology vision, we develop a invariant recognition model by exploiting temporal correlation. Maximizing spatial independence leads to emergence of simple cell properties. Subsequently minimizing the varitation of simple cells outputs over time leads to emergence of invariant features typical of complex cells. Experiments on character images testify recognition is rotation and translation invariant. Our model's plausibility in neurobiology, view is also discussed.

关键词： biology vision ivariant feature temporal correlation object recognition

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