行人检测新方法：HOG特征在CVPR05论文中的应用

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"cvpr05 Histograms of Oriented Gradients for Human Detection.pdf" 这篇论文《Histograms of Oriented Gradients for Human Detection》是行人检测领域的一篇经典之作，作者Navneet Dalal和Bill Triggs来自法国INRIA Rhône-Alpes研究所。他们通过实验对比了不同的特征集在行人检测中的表现，特别强调了基于方向梯度直方图（Histograms of Oriented Gradients，简称HOG）的描述符在行人检测任务中的优越性。 HOG特征是一种用于计算机视觉中物体检测的强大工具，尤其在行人检测上表现出色。它通过计算图像局部区域内的方向梯度和它们的分布来提取特征。论文中，作者详细探讨了HOG特征计算的各个阶段如何影响最终的检测性能，包括： 1. **梯度计算**：精细尺度的梯度计算能够捕获更多的细节信息，这对于识别复杂的形状和姿态变化至关重要。 2. **方向量化**：采用精细的方向binning策略，可以更精确地捕捉到图像边缘的方向信息，提高特征的描述能力。 3. **空间采样**：相对粗略的空间binning有助于减少计算复杂性，同时保持足够的定位精度，确保特征对物体大小的变化具有一定的不变性。 4. **局部对比度归一化**：在重叠的描述子块中进行局部对比度归一化可以增强特征的鲁棒性，减少光照、阴影等因素的影响。论文中提到，新的HOG方法在原始的MIT行人数据库上实现了近乎完美的分离效果。为了进一步挑战该方法，作者构建了一个更具有挑战性的数据集，包含超过1800张注释好的人类图像，这些图像涵盖了更广泛的姿态变化和背景情况。行人检测是计算机视觉中的核心问题之一，HOG特征的提出极大地推动了该领域的进步。此方法不仅在行人检测中得到广泛应用，还启发了许多后续的物体检测算法，如Deformable Part Models (DPM)等。通过深入理解和应用HOG特征，研究者和开发者能够在目标检测、行为分析等领域取得更好的结果。

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.

3 Overview of the Method

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

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行人检测新方法：HOG特征在CVPR05论文中的应用

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