HOG特征在行人检测中的应用与优势

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"Navneet Dalal 和 Bill Triggs 的经典文章《Histograms of Oriented Gradients for Human Detection》深入探讨了使用 HOG 特征进行人体检测的方法。文章首先回顾了现有的边缘和梯度基元描述符，然后通过实验表明，基于梯度方向直方图（Histograms of Oriented Gradients，简称 HOG）的特征网格在人体检测任务上显著优于其他已有的特征集。HOG 特征的关键在于它们能够捕捉图像中的局部形状和纹理信息，这对于识别复杂的对象，如人体，尤其有效。作者研究了计算过程中的每个阶段对性能的影响，包括细尺度梯度、精细的方向量化、相对粗粒度的空间量化以及局部对比度归一化在重叠描述符块中的重要性。这些因素共同作用，使得新方法在原始 MIT 行人数据库上实现了接近完美的分类效果。由于原始数据库的挑战性不足，作者引入了一个更复杂的数据集，包含了超过 1800 张注释了的人体图像，涵盖更大的姿势变化范围和背景，以提高模型的泛化能力。这个新数据集为人体检测提供了更大的挑战，推动了相关领域的研究进步。 HOG 特征是计算机视觉领域中用于人体检测的一种强大工具。它通过计算和积累图像局部区域的梯度方向分布来形成描述符，这些描述符能够有效地捕捉人体各部分的形状信息。HOG 方法的成功在于其对图像细节的敏感性，尤其是在处理光照变化、遮挡和姿势变化时的鲁棒性。因此，HOG 在行人检测、行为分析等众多应用中得到了广泛采用，并且是后续深度学习方法发展的重要基础。"

Histograms of Oriented Gradients for Human Detection

Navneet Dalal and Bill Triggs

INRIA Rhˆone-Alps, 655 avenue de l’Europe, Montbonnot 38334, France

{Navneet.Dalal,Bill.Triggs}@inrialpes.fr, http://lear.inrialpes.fr

Abstract

We study the question of feature sets for robust visual ob-

ject recognition, adopting linear SVM based human detec-

tion as a test case. After reviewing existing edge and gra-

dient based descriptors, we show experimentally that grids

of Histograms of Oriented Gradient (HOG) descriptors sig-

niﬁcantly outperform existing feature sets for human detec-

tion. We study the inﬂuence of each stage of the computation

on performance, concluding that ﬁne-scale gradients, ﬁne

orientation binning, relatively coarse spatial binning, and

high-quality local contrast normalization in overlapping de-

scriptor blocks are all important for good results. The new

approach gives near-perfect separation on the original MIT

pedestrian database, so we introduce a more challenging

dataset containing over 1800 annotated human images with

a large range of pose variations and backgrounds.

1 Introduction

Detecting humans in images is a challenging task owing

to their variable appearance and the wide range of poses that

they can adopt. The ﬁrst need is a robust feature set that

allows the human form to be discriminated cleanly, even in

cluttered backgrounds under difﬁcult illumination. We study

the issue of feature sets for human detection, showing that lo-

cally normalized Histogram of Oriented Gradient (HOG) de-

scriptors provide excellent performance relative to other ex-

isting feature sets including wavelets [17,22]. The proposed

descriptors are reminiscent of edge orientation histograms

[4,5], SIFT descriptors [12] and shape contexts [1], but they

are computed on a dense grid of uniformly spaced cells and

they use overlapping local contrast normalizations for im-

proved performance. We make a detailed study of the effects

of various implementation choices on detector performance,

taking “pedestrian detection” (the detection of mostly visible

people in more or less upright poses) as a test case. For sim-

plicity and speed, we use linear SVM as a baseline classiﬁer

throughout the study. The new detectors give essentially per-

fect results on the MIT pedestrian test set [18,17], so we have

created a more challenging set containing over 1800 pedes-

trian images with a large range of poses and backgrounds.

Ongoing work suggests that our feature set performs equally

well for other shape-based object classes.

We brieﬂy discuss previous work on human detection in

§2, give an overview of our method §3, describe our data

sets in §4 and give a detailed description and experimental

evaluation of each stage of the process in §5–6. The main

conclusions are summarized in §7.

2 Previous Work

There is an extensive literature on object detection, but

here we mention just a few relevant papers on human detec-

tion [18,17,22,16,20]. See [6] for a survey. Papageorgiou et

al [18] describe a pedestrian detector based on a polynomial

SVM using rectiﬁed Haar wavelets as input descriptors, with

a parts (subwindow) based variant in [17]. Depoortere et al

give an optimized version of this [2]. Gavrila & Philomen

[8] take a more direct approach, extracting edge images and

matching them to a set of learned exemplars using chamfer

distance. This has been used in a practical real-time pedes-

trian detection system [7]. Viola et al [22] build an efﬁcient

moving person detector, using AdaBoost to train a chain of

progressively more complex region rejection rules based on

Haar-like wavelets and space-time differences. Ronfard et

al [19] build an articulated body detector by incorporating

SVM based limb classiﬁers over 1

and 2

order Gaussian

ﬁlters in a dynamic programming framework similar to those

of Felzenszwalb & Huttenlocher [3] and Ioffe & Forsyth

[9]. Mikolajczyk et al [16] use combinations of orientation-

position histograms with binary-thresholded gradient magni-

tudes to build a parts based method containing detectors for

faces, heads, and front and side proﬁles of upper and lower

body parts. In contrast, our detector uses a simpler archi-

tecture with a single detection window, but appears to give

signiﬁcantly higher performance on pedestrian images.

3OverviewoftheMethod

This section gives an overview of our feature extraction

chain, which is summarized in ﬁg. 1. Implementation details

are postponed until §6. The method is based on evaluating

well-normalized local histograms of image gradient orienta-

tions in a dense grid. Similar features have seen increasing

use over the past decade [4,5,12,15]. The basic idea is that

local object appearance and shape can often be characterized

rather well by the distribution of local intensity gradients or

Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)

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HOG特征在行人检测中的应用与优势

论文解读《Histograms of Oriented Gradients for Human Detection》.pdf

人体检测梯度方向直方图 Histograms of Oriented Gradients for Human Detection

Histograms of Oriented Gradients for Human Detection论文

[2005 CVPR] Histograms of Oriented Gradients for Human Detection

HOG--Histograms of Oriented Gradients for Human Detection.

计算方向梯度直方图，基于经典论文Histograms of Oriented Gradients for Human Detection

hogsvmmatlab代码-objective-detection-source:目标检测源

histogramofgradients-120212033438-phpapp02.rar_hog_human

(源码)基于QT框架的云存储系统.zip

2010-2023国自科立项名单管理学部.xlsx

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