Description: 基于HOG的行人检测，作者的源代码有错误，现代码已经进行了改变，并可以调试通-Histograms of Oriented Gradients for Human Detection, the writer s code has some errors, and I have correct the errors, and the code is right under C++ buidler Platform: |
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Author:李燕 |
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Description: 本文运用尺度空间理论检测人体，通过集成
面向梯度与histogramof尺度空间理论
-Human detection is the task of finding presence and position
of human beings in images. In this paper, we apply
scale space theory to detecting human in still images. By integrating
scale space theory with histogramof oriented gradients
(HOG), we designed a new feature descriptor called
scale space histogram of oriented gradients (SS-HOG). SSHOG
focus on the multiple scale property of describe an
object. Using HOGs at multiple scale, SS-HOG encodes
more information to discriminate human bodies from other
object types than traditional uni-scale HOGs. Experiments
on INRIA person dataset demonstrate the effectiveness of
our method. Platform: |
Size: 142336 |
Author:谷川 |
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Description: 利用级联SVM的人体检测方法从图像中检测出人体是计算机视觉应用中的关键步骤。通过一个由简到繁的级联线性SVM分类器将级
联拒绝的机制与梯度方向直方图特征相结合,实现了一个准确和快速的人体检测器,整个检测器由级联的线性
SVM分类器组成。实验结果表明,在保持Dalal算法检测准确性的同时,大幅的提高了检测速度,每秒平均可以处
理12帧左右的320 ×240的图像。-Human detection using cascade SVM method detected from the images of computer vision applications, the human body is a key step. From the simple to the complex through a cascade of linear SVM classifier with the cascade mechanism of rejection combined histograms of oriented gradients to achieve an accurate and rapid detection of the human body, the detector by a cascade of linear SVM classifier component. The results show that the accuracy in maintaining Dalal algorithm to detect the same time, substantially increase the detection rate of 12 frames per second on average can process about 320 × 240 image. Platform: |
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Author:lilin |
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Description: HOG calculator, takes filename as input and calculates Histogram of oriented gradients from the input image. Platform: |
Size: 1024 |
Author:amol |
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Description: Histograms of Oriented Gradients for Human
Detection’ versus ’Fast Human Detection Using a
Cascade of Histograms of Oriented Gradients’-Histograms of Oriented Gradients for Human
Detection’ versus ’Fast Human Detection Using a
Cascade of Histograms of Oriented Gradients’ Platform: |
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Author: |
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Description: Face detection algorithms are widely used in computer vision as they provide fast and reliable results depending on the application
domain. A multi view approach is here presented to detect frontal and profile pose of people face using Histogram of Oriented Gradients, i.e. HOG, features. A K-mean clustering technique is used in a cascade of HOG feature classifiers to detect faces. The evaluation of the algorithm shows similar performance in terms of detection rate as state of the art algorithms. Moreover, unlike state of the art algorithms,our system can be quickly trained before detection is possible. Performance is considerably increased in terms of lower computational cost and lower false detection rate when combined with motion constraint given by moving objects in video sequences. The detected HOG features are integrated within a tracking framework and allow reliable face tracking results in several tested surveillance video sequences. Platform: |
Size: 293888 |
Author:linuszhao |
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Description: This paper presents a method that combines
colour and motion information to track pedestrians in video sequences captured by a fixed camera. Pedestrians are firstly
detected using the human detector proposed by Dalal and Triggs which involves computing the histogram of oriented gradients descriptors and classification using a linear support
vector machine. For the colour-based model, we extract a 4-dimensional colour histogram for each detected pedestrian window and compare these colour histograms between consecutive
video frames using the Bhattacharyya coefficient. For the motion model, we use a Kalman filter which recursively predicts and updates the estimates of the positions of pedestrians in the video frames. We evaluate our tracking method using videos from two pedestrian video datasets from the
web. Our experimental results show that our tracking method outperforms one that uses only colour information and can handle partial occlusion. Platform: |
Size: 484352 |
Author:linuszhao |
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Description: 该程序分别提取正负样本图像的HOG和LBP特征，利用支持向量机进行样本训练，得到行人分类器。利用训练好的分类器进行检测，实验结果表明，该方法可以有效检测出图像中的行人，并达到了较好的检测结果。-A novel approach based on combining Histogram of oriented gradients (HOG) and LocalBinary Pattern(LBP) is suggested in the program.Also liner SVM is acted as the classifier,and the experiment suggests that the method can better deal with human detection. Platform: |
Size: 14336 |
Author:邵文 |
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Description: 一种基于HOG-LBP特征的人脸检测方法，对于遮挡的人体非常有效。-By combining Histograms of Oriented Gradients (HOG)
and Local Binary Pattern (LBP) as the feature set, we pro-
pose a novel human detection approach capable of handling
partial occlusion. Two kinds of detectors, i.e., global de-
tector for whole scanning windows and part detectors for
local regions, are learned from the training data using lin-
ear SVM. For each ambiguous scanning window, we con-
struct an occlusion likelihood map by using the response
of each block of the HOG feature to the global detector.
The occlusion likelihood map is then segmented by Mean-
shift approach. The segmented portion of the window with
a majority of negative response is inferred as an occluded
region. If partial occlusion is indicated with high likelihood
in a certain scanning window, part detectors are applied
on the unoccluded regions to achieve the fi nal classifi ca-
tion on the current scanning window. With the help of the
augmented HOG-LBP feature and the global-part occ Platform: |
Size: 3078144 |
Author:有来有去 |
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Description: 在阅读过2005年的HOG论文后写的梯度方向直方图的代码-I wrote the code of HOG after I read the paper which name was Histograms of Oriented Gradients published at CPRV05. Platform: |
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Author:高上 |
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Description: 提取图像方向梯度的直方图特征，image processing（图像处理）-HOG feature extraction
Description
This program is used to extract HOG(Histograms of oriented gradients) features from images. The integral histogram is used for fast histogram extraction. Both APIs and binary utility are provided.
It is written in C++.
References
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005., vol. 1, 2005.
How to use:
1. To compile the code, you need download Boost library(http://www.boost.org/) with version 1.35 or higher.
2. To run the binary, you should make sure Boost so library is avaliable
3. The program are tested with Ubuntu 7.04, however, the API files are compitable with Windows also, and the binary utility hog.cpp depends on unistd.h.
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Author:朱 |
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Description: 为了准确地对监控场景中的运动目标进行语义上的分类， 提出了一种基于聚类的核主成分分析梯度方向直方图和二叉决策树支持向量机的运动目标分类算法．利用背景减法提取运动目标前景区域， 并识别出潜在候选运动目标．利
用提出的基于聚类的核主成分分析的梯度直方图描述子提取候选运动目标的特征， 以较低维数的数据有效地描述运动目标的有效特征． 将提取的运动目标特征输入二叉决策树支持向量机， 实现多类目标的准确分类． 通过在不同视频序列上的实验验证， 提出的算法对运动目标进行较好地分类， 而且在运算速度方面较传统目标分类方法有了明显的提高． 实验结果证明了算法对运动目标分类具有较好的准确性 可靠性和鲁棒性．-For the purpose of semantically classifying moving objects accurately in a surveillance scene，a moving objects classification method based on the clustered kernel principal component analysis ( CKPCA) of the histogram
of oriented gradients ( HOG) and support vector machine ( SVM) was proposed． Firstly，the moving areas in the
foreground were extracted by means of the background subtraction method，and some of them were identified as potential candidates of moving objects． Secondly，the characteristics of the moving objects were obtained by the CKPCA- HOG descriptor，which could describe the moving objects＇ effective features at a lower data dimension． Finally，the data characteristics were fed into a binary SVM decision tree，and the final multi- class classification results were obtained accurately． After verifying different video sequences，the algorithm was able to classify moving targets very well． Compared with traditional classification methods，the proposed method makes obvious improv Platform: |
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Author:高峰 |
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Description: We present a parallel implementation of a histogram-based particle filter for object tracking on smart
cameras based on SIMD processors. We specifically focus on parallel computation of the particle weights
and parallel construction of the feature histograms since these are the major bottlenecks in standard
implementations of histogram-based particle filters. The proposed algorithm can be applied with any histogram-
based feature sets—we show in detail how the parallel particle filter can employ simple color histograms
as well as more complex histograms of oriented gradients (HOG). The algorithm was successfully
implemented on an SIMD processor and performs robust object tracking at up to 30 frames per second—a
performance difficult to achieve even on a modern desktop computer. Platform: |
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Author:gugu |
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Description: Histogram of Oriented Gradients (HOG) are feature descriptors used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image. This method is similar to that of edge orientation histograms, scale-invariant feature transform descriptors, and shape contexts, but differs in that it is computed on a dense grid of uniformly spaced cells and uses overlapping local contrast normalization for improved accuracy. Platform: |
Size: 1024 |
Author:Mohammad |
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Search - Oriented gradients