行人检测论文集合(转)

利用Hog特征和SVM分类器进行行人检测 之前介绍过Hog特征(http://blog.youkuaiyun.com/carson2005/article/details/7782726),也介绍过SVM分类器(http://blog.youkuaiyun.com/carson2005/article/details/6453502 );而本文的目的在于介绍利用Hog特征和SVM分类器来进行行人检测。         在2005年CVPR上,来自法国的研究人员 阅读详情

from (视觉模式分析实验室 http://1817104713.qzone.qq.com)


行人数据库

INRIA person database: http://pascal.inrialpes.fr/data/human/

Pen-Fudan: http://www.cis.upenn.edu/~jshi/ped_html/

Caltech Pedestrian: http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/

CVC-CER-01 Pedestrian Database:

CVC Virtual Pedstrain Database: http://www.cvc.uab.es/adas/index.php?section=other_datasets

 http://www.cvpapers.com/datasets.html

TUD-Brussels 见C. Wojej, S. Walk, B. Schiele, "Multi-cue onboard pedestrian detection," in Proc. CVPR, 2009

Caltech-Janpan 见P. Dollar, C. Wojek, B. Schiele, P. Perona, "Pedestrian detection: A benchmark," in Proc. CVPR 2009.

ETH 见A. Ess, B. Leibe, and L.Van Gool, "Depth and appearance for mobile scene analysis," in Proc. ICCV, 2007.

Daimler 见M. Enzweiler and D.M. Gavrila, "Monocular pedestrian detection: suvey and experiments," IEEE T-PAMI, 2009.

 

http://www.edgar-seemann.de/pd/index.py

R. M. Rao, D. Vazquez, A. M. Lopez, "Opponent Color for Human Detection", Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA, 2011

2012

Piotr Dollar, Christian Wojek, Bernt Schiele, and Pietro Persona, "Pedestrian detection: an evaluation of the state of the art," PAMI, vol. 34, no. 4, pp. 743-761, 2012.

Grégory Rogez, Jonathan Rihan, Carlos Orrite-Uruuela & Philip H. S. Torr, "Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors," International Journal of Computer Vision, vol. 99, no. 1, 2012.

2011

"Describing People: A Poselet-Based Approach to Attribute Classification"  L. Bourdev, S. Maji, and J. Malik.   ICCV 2011  [pdf]

 

2010

D. Geronimo, "A Global Approach to Vision Based Pedestrian Detection for Advanced Driver Assistance Systems", PhD Thesis. Computer Vision Center Barcelona, February 2010., 2010

D. Geronimo and A.M. Lopez and A.D. Sappa and T. Graf, "Survey of Pedestrian Detection for Advanced Driver Assistance Systems", PAMI, 2010

D. Gernimo, A.D. Sappa, D. Ponsa and A.M. Lopez, "2D-3D based on-board pedestrian detection system", In Computer Vision and Image Understanding (Special Issue on Intelligent Vision Systems), 2010

J. Marin, D. Vazquez, D. Gerenimo and A.M. Lopez, "Learning Appearance in Virtual Scenarios for Pedestrian Detection", CVPR, 2010

L. Bourdev and S. Maji and T. Brox, T. and J. Malik, "Detecting People Using Mutually Consistent Poselet Activations", ECCV, 2010

P. Felzenszwalb and R. Girshick, and D. McAllester, "Cascade Object Detection with Deformable Part Models", CVPR, 2010

P. Felzenszwalb and R. Girshick and D. McAllester and D. Ramanan, , "Object Detection with Discriminatively Trained Part-Based Models", PAMI, 2010

S. Walk and N. Majer and K. Schindler and B. Schiele, "New Features and Insights for Pedestrian Detection", CVPR, 2010

2009

M. Andriluka and S. Roth and B. Schiele, "Pictorial Structures Revisited: People Detection and Articulated Pose Estimation", CVPR, 2009

C. Wojek and S. Walk and B. Schiele, "Multi-Cue Onboard Pedestrian Detection", CVPR, 2009

P. Dollar and C. Wojek and B. Schiele and P. Perona, "Pedestrian Detection: A Benchmark", CVPR, 2009

X. Wang and T.X. Han and S.Yan, "An HOG-LBP human detector with partial occlusioin handling", CVPR, 2009

L. Bourdev and J. Malik, "Poselets: Body part detectors trained using 3d human pose annotations", ICCV, 2009

M. Ozusayal and V. Lepetit and P. Fua, "Pose estimation for category specific multiview object localization", CVPR, 2009

Duc Thanh Nguyen, Wanqing Li, and Philip Ogunbona, "An Improved Template Matching Method for Object Detection", ACCV, 2009

2008

M. Andriluka and S. Roth and B. Schiele, "People-Tracking-by-Detection and People-Detection-by-Tracking", CVPR, 2008

M. Enzweiler and D. Gavrila, "A Mixed Generative-Discriminative Framework for Pedestrian Classification", CVPR, 2008

P. Felzenszwalb and D. McAllester and D. Ramanan, "A discriminatively trained, multiscale, deformable part model", CVPR, 2008

S. Maji and A. C. Berg and J. Malik, "Classification using intersection kernel support vector machines is efficient", CVPR, 2008

2007

Edgar Seemann, "Pedestrian Detection in Crowded Street Scenes", PhD Thesis, 2007

B. Wu and R. Nevatia, "Simultaneous Object Detection and Segmentation by Boosting Local Shape Feature based Classifier", CVPR, 2007

D. Gavrila and S. Munder, "Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle", IJCV, 2007

A. Bissacco and M. Yang and S. Soatto, "Detecting Humans via Their Pose", NIPS, 2007

D. Ger\\'onimo and A. Sappa and A. L\\'opez and D. Ponsa, "Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection", ICVS, 2007

B. Leibe and N. Cornelis and K. Cornelis and L. Van Gool, "Dynamic 3D Scene Analysis from a Moving Vehicle", CVPR, 2007

L. Wang and J. Shi and G. Song and I. Shen, "Object Detection Combining Recognition and Segmentation", ACCV, 2007

Sergiu Nedevschi, Corneliu Tomiuc and Silviu Bota, "Stereo Based Pedestrian Detection for Collision Avoidance Applications", ICRA Workshop: Planning, Perception and Navigation for Intelligent Vehicles, 2007

B. Wu and R. Nevatia, "Cluster boosted tree classifier for multi-view, multi-pose object detection", ICCV, 2007

O. Tuzel and F. Porikli and P. Meer, "Human detection via classification on riemannian manifolds", CVPR, 2007

2006

B. Wu and R. Nevatia, "Tracking of Multipl", CVPR, 2006

E. Seemann and B. Leibe and B. Schiele, "Multi-Aspect Detection of Articulated Objects", CVPR, 2006

E. Seemann and B. Schiele, "Cross-Articulation Learning for Robust Detection of Pedestrians", DAGM, 2006

N. Dalal and B. Triggs and C. Schmid, "Human Detection Using Oriented Histograms of Flow and Appearance", ECCV, 2006

Q. Zhu and M. Yeh and K. Cheng and S. Avidan, "Fast Human Detection Using a Cascade of Histograms of Oriented Gradients", CVPR, 2006

S. Munder and D. Gavrila, "An Experimental Study on Pedestrian Classification", PAMI, 2006

A. Torralba and K. P. Murphy and W. T. Freeman, "Sharing visual features for multiclass and multiview object detection", submitted to PAMI, 2006

J. Berclaz and F. Fleuret and P. Fua, "Robust People Tracking with Global Trajectory Optimization", CVPR, 2006

A. Opelt and A. Pinz and A. Zisserman, "A Boundary-Fragment-Model for Object Detection", ECCV, 2006

K. Mikolajczyk and B. Leibe and and B. Schiele, "Multiple Object Class Detection with a Generative Model", CVPR, 2006

K. Grauman and T. Darrell, "Unsupervised Learning of Categories from Sets of Partially Matching Image Features", CVPR, 2006

B. Leibe and K. Mikolajczyk and B. Schiele, "Segmentation Based Multi-Cue Integration for Object Detection", BMVC, 2006

M. Fritz and B. Schiele, "Towards Unsupervised Discovery of Visual Categories", DAGM, 2006

A. Agarwal and B. Triggs, "Hyperfeatures - multilevel local coding for visual recognition", ECCV, 2006

S. Lazebnik and C. Schmid and J. Ponce, "A discriminative framework for texture and object recognition using local image features", ECCV Workshop: Towards category-level object recognition, 2006

D. Hoiem and A. Efros and M. Hebert, "Putting Objects in Perspective", CVPR, 2006

M. Dimitrijevic and V. Lepetit and P. Fua, "Human Body Pose Detection Using Bayesian Spatio-Temporal Templates", CVIU, 2006

2005

Edgar Seemann, Bastian Leibe and Bernt Schiele, "Pedestrian Detection in Crowded Scenes", CVPR, 2005

M. Szarvas and a. Yoshizawa and M Yamamoto and J. Ogata, "Pedestrian Detection with Convolutional Neural Networks", IVS, 2005

B. Wu and R. Nevatia, "Detection of Multipl", ICCV, 2005

A. Micilotta and E. Ong and R. Bowden, "Detection and Tracking of Humans by Probabilistic Body Part Assembly", BMVC, 2005

K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors", PAMI, 2005

B. Leibe and E. Seemann and B. Schiele, "Pedestrian Detection in Crowded Scenes", CVPR, 2005

M. Fritz and B. Leibe and B. Schiele, "Integrating Representative and Discriminant Models for Object Category Detection", ICCV, 2005

E. Seemann and B. Leibe and K. Mikolajczyk and B. Schiele, "An Evaluation of Local Shape-Based Features for Pedestrian Detection", BMVC, 2005

N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection", CVPR, 2005 (另外,请搜索下载该作者在2006年的博士论文)

K. Smith and D. G.-Perez and J.-M. Odobez, "Using Particles to Track Varying Numbers of Interacting People", CVPR, 2005

J. R. Peter and H. Tu and N. Krahnstoever, "Simultaneous Estimation of Segmentation and Shape", CVPR, 2005

A. Berg and T. Berg and J. Malik, "Shape Matching and Object Recognition using Low Distortion Correspondence", CVPR, 2005

G. Bouchard and B. Triggs, "Hierarchical Part-Based Visual Object Categorization", CVPR, 2005

E. Sudderth and A. Torralba and W. Freeman and A. Willsky, "Learning Hierarchical Models of Scene", ICCV, 2005

Y. Wu and T. Yu and G. Hua, "A Statistical Field Model for Pedestrian Detection", CVPR, 2005

P. Roth and H. Grabner and D. Skovcaj and H. Bischof and A. Leonardis, "On-line conservative learning for person detection", PETS, 2005

2004

D. Gavrila and J. Giebel and S. Munder, "Vision-Based Pedestrian Detection: The PROTECTOR System", IVS, 2004

K. Mikolajczyk and C. Schmid and A. Zisserman, "Human detection based on a probabilistic assembly of robust part detectors", ECCV, 2004

Hannes Kruppa, "Object Detection using Scale-specific Boosted Parts and a Bayesian Combiner", , 2004

A. Shashua and Y. Gdalyahu and G. Hayon, "Pedestrian Detection for Driving Assistance Systems: Single-frame Classification and System Level Performance", IVS, 2004

G. Mori and X. Ren and A.A. Efros and J. Malik, "Recovering Human Body Configurations: Combining Segmentation and Recognition", CVPR, 2004

D. Lowe, "Distinctive image features from scale-invariant keypoints", IJCV, 2004

B. Leibe and B. Schiele, "Scale Invariant Object Categorization Using a Scale-Adaptive Mean-Shift Search", DAGM, 2004

B. Leibe and A. Leonardis and B. Schiele, "Combined Object Categorization and Segmentation with an Implicit Shape Model", ECCV Workshop on Statistical Learning in Computer Vision, 2004

Bastian Leibe, "Interleaved Object Categorization and Segmentation", , 2004

S. Agarwal and A. Atwan and D. Roth, "Learning to Detect Objects in Images via a Spars", PAMI, 2004

A. Torralba and K. Murphy and W. Freeman, "Sharing features: efficient boosting procedures for multiclass object detection", CVPR, 2004

P. Viola and M. Jones, "Robust Real-Time Face Detection", IJCV, 2004

C. Huang and H. Ai and B. Wu and S. Lao, "Boosting Nested Cascade Detector for Multi-View Face Detection", ICPR, 2004

E. Borenstein and S. Ullman, "Learning to Segment", ECCV, 2004

E. Borenstein and E. Sharon and S. Ullman, "Combining Top-Down and Bottom-Up Segmentations", POCV, 2004

T. Jebara and R. Kondor and A. Howard, "Probability Product Kernels", JMLR, 2004

N. Vasconcelos and P. Ho and P. Moreno, "The Kullback-Leibler Kernel as a Framework for Discriminant and Localized Representations for Visual Recognition", ECCV, 2004

F. Perreira and A. Bernal and K. Crammer and R. McDonald, "Linear models for structure prediction", NIPS Workshop on Graphical Models and Kernels, 2004

M. Nilsback and B. Caputo, "Cue Integration through Discriminative Accumulation", CVPR, 2004

J. Vogel, "Semantic Scene Modeling and Retrieval", , 2004

S. Boughorbel and J.-P. Tarel and F. Fleuret, "Non-Mercer Kernels for SVM Object Recognition", BMVC, 2004

T. Zhao and R. Nevatia, "Tracking multiple humans in crowded environment", CVPR, 2004

K. Okuma and A. Taleghani and N. de Freitas and J. Little and D. Lowe, "A boosted particle filter: multitarget detection and tracking", ECCV, 2004

F.-F. Li, "Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories", CVPR Workshop On Generative-Model Based Vision, 2004

B. Caputo and C. Wallraven and M. Nilsback, "Object Categorization via Local Kernels", ICPR, 2004

J. Giebel and D. Gavrila and C. Schn\\"orr, "A Bayesian Framework for Multi-cue 3D Object Tracking", ECCV, 2004

2003

P. Viola and M.J. Jones and D. Snow, "Detecting Pedestrians Using Patterns of Motion and Appearance", ICCV, 2003

M. Bertozzi and A. Broggi and R. Chapuis and F. Chausse and A. Fascioli and A. Tibaldi, "Shape-based pedestrian detection and localization", ITS, 2003

B. Leibe and B. Schiele, "Interleaved Object Categorization and Segmentation", BMVC, 2003

A. Thayananthan and B. Stenger and P.H.S. Torr and R.Cipolla, "Shape Context and Chamfer Matching in Cluttered Scenes", CVPR, 2003

P. Felzenszwalb and D. Huttenlocher, "Pictorial structures for object recognition", IJCV, 2003

M. Vidal-Naquet and S. Ullman, "Object Recognition with Informative Features and Linear Classification", ICCV, 2003

S.X. Yu and J. Shi, "Object-Specific Figure-Ground Segregation", CVPR, 2003

D. Sko\\v{c}aj and A. Leonardis, "Weighted and robust incremental method for subspace learning", ICCV, 2003

F. Li and R. Fergus and P. Perona, "A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories", ICCV, 2003

Z. Tu and X. Chen and A. Yuille and S. Zhu, "Image Parsing: Unifying Segmentatio", ICCV, 2003

A. Mittal and L. Davis, "M2tracker: A multi-view approach to segmenting and tracking people in a cluttered scene", IJCV, 2003

R. Fergus and A. Zisserman and P. Perona, "Object Class Recognition by Unsupervised Scale-Invariant Learning", CVPR, 2003

L. Fei-Fei and R. Fergus and P. Perona, "A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories", ICCV, 2003

C. Wallraven and B. Caputo and A. Graf, "Recognition with Local Features: the Kernel Recipe", ICCV, 2003

G. Dork\\'o and C. Schmid, "Selection of Scale-Invariant Parts for Object Class Recognition", ICCV, 2003

Y. Fang and K. Yamada and Y. Ninomiya and B. Horn and I. Masaki, "Comparison between infrared-image-based and visible-image-based approaches for pedestrian detection", IVS, 2003

A. Torralba, "Contextual priming for object detection", IJCV, 2003

2002

R. Ronfard and C. Schmid and B. Triggs, "Learning to Parse Pictures of People", ECCV, 2002

G. Mori and J. Malik, "Estimating Human Body Configurations using Shape Context Matching", ECCV, 2002

S. Belongie and J. Malik and J. Puzicha, "Shape Matching and Object Recognition Using Shape Contexts.", PAMI, 2002

S. Ullman and M. Vidal-Naquet and E. Sali, "Visual features of intermediate complexity and their use in classification", Nature Neuroscience, 2002

K. Tsuda and M. Kawanabe and G. R\\"atsch and S. Sonnenburg and K.-R. M\\"uller, "A new discriminative kernel from probabilistic models", Neural Computation, 2002

A. Garg and S. Agarwal and T. Huang, "Fusion of Global and Local Information for Object Detection", ICPR, 2002

S. Agarwal and D. Roth, "Learning a Sparse Representation for Object Detection", ECCV, 2002

E. Borenstein and S. Ullman, "Class-Specifi", ECCV, 2002

2001

A. Mohan and C. Papageorgiu and T. Poggio, "Example-Based Object Detection in Images by Componentes", PAMI, 2001

S. Ioffe and D. Forsyth, "Probabilistic methods for finding people", IJCV, 2001

K. Mikolajczyk and C. Schmid, "Indexing based on scale invariant interest points", ICCV, 2001

A. Berg and J. Malik, "Geometric Blur for Template Matching", CVPR, 2001

P. Felzenszwalb, "Learning Models for Object Recognition", CVPR, 2001

D. Comaniciu and V. Ramesh and P. Meer, "The Variable Bandwidth Mean Shift and Data-Driven Scale Selection", ICCV, 2001

A. Ng and M. Jordan, "On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes", NIPS, 2001

B. Heisele and T. Serre and M. Pontil and T. Poggio, "Component-Based Face Detection", CVPR, 2001

M. Isard and J. MacCormick, "BraMBLe: A Bayesian Multiple-Blob Tracker", ICCV, 2001

S. Khan and O. Javed and M. Shah, "Tracking in uncalibrated cameras with overlapping field of view", PETS, 2001

T. Kadir and M. Brady, "Scal", IJCV, 2001

K.-R. Mueller and S. Mika and G. Raetsch and K. Tsuda and B. Schoelkopf, "An introduction to kernel-based learning algorithms", IEEE Neural Networks, 2001

2000

D. Gavrila, "Pedestrian Detection from a Moving Vehicle", ECCV, 2000

L. Zhao and C. Thorpe, "Stereo and Neural Network-based Pedestrian Detection", IEEE Transactions on Intelligent Transportation Systems, 2000

C. W\\"ohler and U. Kressel and J.K. Anlaur, "Pedestrian recognition by classification of image sequences - Global approaches vs. local spatio-temporal processing", ICPR, 2000

C. Papageorgiou and T. Poggio, "A Trainable System for Object Detection", IJCV, 2000

A. Broggi and M. Bertozzi and A. Fascioli and M. Sechi, "Shape-based pedestrian detection", IV, 2000

P. Felzenszwalb and D. Huttenlocher, "Efficient matching of pictorial structures", CVPR, 2000

H. Schneiderman and T. Kanade, "A Statistical Method of 3D Object Detection Applied to Faces and Cars", CVPR, 2000

M. Weber and M. Welling and P. Perona, "Unsupervised learning of object models for recognition", ECCV, 2000

M. Weber and M. Welling and P. Perona, "Towards Automatic Discovery of Object Categories", CVPR, 2000

1999

D. Gavrila, "Real-Time Object Detection for Smart Vehicles", ICCV, 1999

C. Papageorgiou and T. Poggio, "Trainable Pedestrian Detection", ICIP, 1999

J. Rehg and K. Murphy and P. Fieguth, " Vision-based speaker detection using bayesian networks", ICCV, 1999

C. Stauffer and W. Grimson, "Adaptive background mixture models for realtime tracking", CVPR, 1999

D. Comaniciu and P. Meer, "Distribution Free Decomposition of Multivariate Data", Pattern Analysis and Applications, 1999

1998

D. Gavrila, "Multi-feature Hierarchical Template Matching Using Distance Transforms", ICPR, 1998

T. Jaakkola and D. Haussler, "Exploiting generative models in discriminative classifiers", NIPS, 1998

M. Pontil and A. Verri, "Support Vector Machines for 3D Object Recognition", PAMI, 1998

Q. Cai and J. Aggarwal, "Automatic tracking of human motion in indoor scenes across multiple synchronized video streams", ICCV, 1998

M. Burl and M. Weber and P. Perona, "A Probabilistic Approach to Object Recognition using Local Photometry and Global Geometry", ECCV, 1998

C. Schmid and R. Mohr and C. Bauckhage, "Comparing and Evaluating Interest Points", ICCV, 1998

T. Lindeberg, "Feature Detection with Automatic Scale Selection", IJCV, 1998

1997

M. Oren and C. Papageorgiou and P. Sinha and E. Osuna and T. Poggio, "Pedestrian detection using wavelet templates", CVPR, 1997

D. Forsyth and M. Fleck, "Body plans", CVPR, 1997

C. Schmid and R. Mohr, "Local Grayvalue Invariants for Image Retrieval", PAMI, 1997

C.F. Olson and D.P. Huttenlocher, "Automatic Target Recognition by Matching Oriented Edge Pixels", TIP, 1997

1995

Y. Freund and R. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting", European Conference on Computational Learning Theory, 1995

A. Leonardis and A. Gupta and R. Bajcsy, "Segmentation of Range Images as the Search for Geometric Parametric Models", IJCV, 1995

T. Cootes and C. Taylor and D. Cooper and J. Graham, "Active Shape Models - Their Training and Application", CVIU, 1995

1991

M. Turk and A. Pentland, "Face recognition using eigenfaces", CVPR, 1991

1988

C. Harris and M. Stephens, "A combined corner and edge detector", Alvey Vision Conference, 1988

G. Borgefors, "Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm", PAMI, 1988

1987

J. Connell and M. Brady, "Generating and Generalizing Models of Visual Objects", Artificial Intelligence, 1987

1986

J. Canny, "A Computational Approach To Edge Detection", PAMI, 1986

1984

L. Valiant, "A theory of the learnable", Communications of the ACM, 1984

1983

A. Witkin, "Scale-Space Filtering", IJCAI, 1983

1982

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1981

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1980

C. de Rham, "La Classification Hi\\'erarchique Ascendante Selon la M\\'ethode des Voisins R\\'eciproques", CAD, 1980

1977

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1973

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1971

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1943

A. Bhattacharyya, "On a Measure of Divergence between two Statistical Populations Defined by their Probability Distributions", Bull. Calcutta Math Soc., 1943

行人异常行为检测 行人异常行为检测什么是行人异常检测异常行为意义分类按行为定义的方法基于定义规则的方法基于概率的统计学方法学习正常模型学习所有数据基于机器学习的方式无监督有监督半监督基于选择的特征局部运动特征轨迹数据集参考待调研 什么是行人异常检测 异常行为 在某一场景中不寻常的位置或不寻常的时刻发生的动作 在某一场景中正常位置、正常时刻发生的不寻常动作 意义 分析视频监控,提高安保人员效率 分类 按行为定义的方法... 阅读详情

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两个传统的图像处理特征,简单有用。 1.HoF(Histogram of Flow) 1)概念 HOF(Histograms of Oriented Optical Flow)是计算光流方向的直方图,和HoG一个概念,通常用于Action Recogntion中。 2)提出原因 一方面,光流对于运动方向,背景变化敏感。另一方面,在动作识别中,相应的光流特征描述子的维度在变化。于是,需要寻找一个基于...

杨杨子江的博客 1732

保姆级教程:用Zemax 18.9复现单模光纤耦合仿真(附康宁SMF-28e参数)

本文提供了一份详细的Zemax 18.9单模光纤耦合仿真教程,以康宁SMF-28e为例,涵盖从软件设置、材料参数输入到系统构建和优化的全流程。通过光束耦合分析和物理光学传播(POP)技术,帮助用户准确模拟光纤耦合效率,并解决常见问题,适用于光学设计初学者和专业人士。

weixin_27330481的博客 316

行人检测论文笔记:Robust Real-Time Face Detection

知识点 傅里叶变换的一个推论: 一个时域下的复杂信号函数可以分解成多个简单信号函数的和,然后对各个子信号函数做傅里叶变换并再次求和,就求出了原信号的傅里叶变换。 卷积定理(Convolution Theorem):信号f和信号g的卷积的傅里叶变换,等于f、g各自的傅里叶变换的积 整个过程的核心就是“(反转),移动,乘积,求和”二维卷积数学定义 二维卷积在图像处理中会经常遇到,图像处理中用到的

JacobKong的博客 2967

功率电感的设计步骤

学习视频原链接截图1截图2。

qq_32419593的博客 4887

[ADS]单端电感和差分电感的感值和Q值计算公式

单端电感和差分电感的感值和Q值计算公式

乾巽的博客 1万+

行人检测论文笔记:How Far are We from Solving Pedestrian Detection?

文章疑问点 Human Baseline 的标准是如何确定的? Ground-truth是什么意思?Groun-truth 指的是正确的标注(真实值) 在有监督学习中,数据是有标注的,以(x, t)的形式出现,其中x是输入数据,t是标注.正确的t标注是ground truth,错误的标记则不是。(也有人将所有标注数据都叫做ground truth)。

JacobKong的博客 4950

SpotBugs使用

spotbugs命令行参数解析

javajingling的专栏 2949

Opencv实现行人检测(HOG + SVM)

1. 理论基础 使用OpenCv进行行人检测的主要思想: HOG + SVM HOG: 方向梯度直方图(Histogram of Oriented Gradient, HOG)特征是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子。HOG特征通过计算和统计图像局部区域的梯度方向直方图来构成特征. SVM: (Support Vector Machine)指的是支持向量机,是...

YancyKahn 2万+

基于高精度三维机器视觉的新能源汽车锂电池表面缺陷检测

在机器视觉行业迅速发展的背景下,市场上提供了功能强大且种类齐全的3D相机系列,可以满足锂电池从电芯到模组各个工艺和工位的3D检测需求。

Hinyeung_Limited的博客 1133

手把手教你用STM32H743+FreeRTOS+LWIP搭建物联网网关(附完整工程)

本文详细介绍了如何使用STM32H743微控制器结合FreeRTOS实时操作系统和LWIP协议栈构建高性能物联网网关。内容涵盖硬件配置、FreeRTOS任务设计、LWIP协议栈优化及物联网协议集成,提供完整工程示例,助力开发者快速实现稳定可靠的网络通信枢纽。

weixin_42623010的博客 503

行人检测论文集合

from (视觉模式分析实验室 http://1817104713.qzone.qq.com) 行人数据库 INRIA person database: http://pascal.inrialpes.fr/data/human/ Pen-Fudan: http://www.cis.upenn.edu/~jshi/ped_html/ Caltech P

三少GG 3588

HyperMesh二次开发实战:自动化Comp与材料属性批量绑定

本文详细介绍了HyperMesh二次开发中自动化Comp与材料属性批量绑定的实战技巧。通过TCL脚本实现批量创建属性卡、智能材料分配和异常处理,大幅提升前处理效率,适用于汽车、航空航天等领域的复杂模型处理。

weixin_29214691的博客 691

python行人检测_基于YOLOv3的行人检测

YOLOv3行人检测本脚本集合主要是针对YOLOv3的两个主流版本(AlexeyAB/darknet & pjreddie/darknet),本身不包含YOLOv3的代码和配置文件,但是根据指引可以完成一个效果较好的行人检测系统。目前主要是以下几个功能:将YOLOv3常用的网址和资料归纳整理了一下;提供代码,帮助从VOC2007/VOC2012/COCO等数据集中提取出person类图片,...

weixin_39583162的博客 4473

技术面 - 手撕算法题整理

这篇博客整理了华为OD面试中涉及的算法题,包括LeetCode热门题目、OD原题和需要手写的算法,如二分查找、冒泡排序等。建议优先刷"hot100"的LeetCode题目,全刷OD原题,以及掌握手撕算法的基本原理。

m0_73659489的博客 1025

jdk-8u144-linux-x64.tar.gz.

该资源为搭建Linux系统Java环境用,资源的后缀为tar.gz格式的压缩包

Deepsort + Yolo 实现行人检测和轨迹追踪

作者 | 李秋键 出品 | AI科技大本营(ID:rgznai100) 引言 行人检测是近年来计算机视觉领域的研究热点,同时也是目标检测领域中的难点。其目的是识别和定位图像中存在的行人,在许多领域中都有广泛的应用。交通安全方面,无人驾驶汽车通过提前检测到行人及时避让来避免交通事故的发生;安防保护方面,通过行人检测来防止可疑人员进入;公共场所管理方面,通过行人检测统计人流量数据,优化人力物力等资源的分配。 对于目标检测的方法,从2013年Ross Girshick提出R-CNN开始,人们在短短几年内相

AI科技大本营 1万+

Camera2Demo

在Camera2 的基础上进行封装,使得Camera2一堆初始化逻辑与Activity分离,降低耦合度,完成预览拍照并保存的操作

HOG+SVM行人检测python实现

HOG+SVM行人检测,NMS,python实现,Windows,INRIA 数据集

weixin_51672709的博客 5402

数学建模模型总结

这是本人在两年内准备参加数学建模竞赛搜集到的模型资料,已经经过了本人的筛选和整理,希望能够对准备参加数学建模竞赛的学弟学妹们提供一些帮助!

ssm框架搭建

这个是一个在idea上成功跑起来的ssm框架项目,保存在这里方便以后使用直接下载

行为识别:行人跌倒检测(含源码)

本文抛砖引玉为大家提供一个行为识别的案例,大家也可以根据该方法实现更多任务,也可以提供一些改进的方法。主要检测7类行为:Standing,Walking,Sitting,Lying Down,Stand up,Sit down,Fall Down。YOLOv3-tiny用于行人检测,DeepSort用于行人跟踪,ST-GCN用于行为检测。 YOLO系列在笔者之前博客有讲解,DeepSort也比较常见,本文主要讲一下ST-GCN方法。...

wq_0708的博客 2万+

深度学习实战(四):行人跟踪与摔倒检测报警

深度学习实战(X):行人跟踪与摔倒检测报警1. 项目简介1.1 相关工作2.方法简介2.1 总体结构2.2 骨架的图结构2.3 空间图卷积网络2.3.1 Sampling Function2.3.2 Weight Function2.3.3 空间图卷积2.3.4 时空模型2.4 划分子集2.5 注意力机制2.6 TCN3. 训练 项目论文:https://arxiv.org/abs/1801.07455 项目源码:评论文章即可获取 1. 项目简介 本项目的目的是为了给大家提供跟多的实战思路,抛砖引玉为大家

与君共勉,一起学习 3704

【机器学习】基于HOG+SVM的行人检测

【机器学习】基于HOG+SVM的行人检测

weixin_58053154的博客 1447
上一篇: 手势识别论文与资源(转)
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