一种多分辨率行人检测方法的改进_如何进行对行人检测的改进资源-CSDN文库

169 浏览量 2020-06-03 23:19:39 上传评论收藏 2.02MB PDF 举报

资源推荐

资源详情

资源评论

Journal of Computer and Communications, 2017, 5, 102-116

http://www.scirp.org/journal/jcc

ISSN Online: 2327-5227

ISSN Print: 2327-5219

DOI: 10.4236/jcc.2017.59007

July 17, 2017

An Improvement of Pedestrian Detection

Method with Multiple Resolutions

Guodong Zhang

, Peilin Jiang

, Kazuyuki Matsumoto

, Minoru Yoshida

, Kenji Kita

Faculy and School of Engineering, Tokushima University, Tokushima, Japan

Xi’an Jiaotong University, Xian, China

Abstract

In object detection, detecting an object with 100 pixels is substantially diffe

ent from detecting

an object with 10 pixels. Many object detection algorithms

assume that the pedestrian scale is fixed during detection,

such as the DPM

detector. However, detectors often give rise to different detection effects u

der the circumstance of different scales. If a detector is used to perform ped

strian detection in different scales, the accuracy of pedestrian detection could

be improved. A multi-resolution DPM pedestrian detection algorithm is pr

posed in this paper.

During the stage of model training, a resolution factor is

added to a set of hidden variables of

a latent SVM model. Then, in the stage of

detection, a standard DPM model is used for the

high resolution objects and a

rigid template is adopted in case of the low resolution objects. In our exper

ments, we

find that in case of low resolution objects the detection accuracy of

a standard DPM model is lower than that of

a rigid template. In Caltech, the

omission ratio of a multi-resolution DPM

detector is 52% with 1 false positive

per image (1FPPI); and the omission ratio rises to 59% (1FPPI)

as far as a

standard DPM detector is concerned. In the large-scale sample set of

Caltech,

the omission ratios given by the multi-resolution and the standard DPM d

tectors are 18% (1FPPI) and 26% (1FPPI), respectively.

Keywords

Deformable Part Model, Pedestrian Detection, Multi-Resolution; Latent SVM

1. Introduction

Pedestrian detection has been a hotspot in computer vision research [1]. The

corresponding detection algorithm has been developed towards high precision

and instantaneity [2] [3]. For a driverless automobile, the usage of which has

How to cite this paper:

Zhang, G.D.,

Jiang,

P.L.,

Matsumoto, K., Yoshida, M. and Kita

(2017)

An Improvement of Pedestrian

Detection Method

with Multiple Resolu-

tions

Journal of Computer and Comm

nications

, 102-116.

https://doi.org/10.4236/jcc.2017.59007

Received:

October 10, 2016

Accepted:

July 14, 2017

Published:

July 17, 2017

7 by authors and

Scientific

Research Publishing Inc.

This work is licensed under the Creative

Commons Attribution International

License (CC BY

4.0).

http://creativecommons.org/licenses/by/4.0/

Open Access

G. D. Zhang et al.

103

become popular nowadays, its intelligent system should be able to detect the lo-

cations and quantities of pedestrians ahead, to analyze the road conditions, and

to guarantee the safety of these pedestrians [4]. For such cases, the pedestrian

detection is an inevitable procedure. The pedestrian detection problem is diffi-

cult because that the target people often have various characteristics and the

surrounding environments also change frequently [5].

The pedestrian sizes in real world are different from each other. Besides the

height diversity of different people, many imaging differences are incurred by

the different distances between people and the camera.

Figure 1 shows a high

resolution corresponds to the large pedestrian scale and a low resolution corres-

ponds to the small pedestrian scale in the process of pedestrian detection.

Pedestrians contain rich information in the case of high resolution [6], and it

is more likely for them to be detected. Even if they are locally overlapped, many

algorithms have the capability to detect these targets [7]. However, in the case

low resolution, the pedestrians which contain a small amount of information

cannot be detected easily. Meanwhile, low resolution pedestrians are very vul-

nerable to the interferences of the surrounding environments. In most cases, a de-

tection algorithm has a much better detection result for the high resolution pede-

strians than that for the low resolution pedestrians. Dalal and Triggs [8] proposed

a HOG detector. If the detection window is fixed to

128 64×

pixels during

training and detection, this detector can generate good effects at the time of de-

tecting pedestrians with pixels greater than

128 64×

. However, when the target

pedestrians are smaller than

128 64×

, the detector almost fails to detect any pe-

destrian. Although the target can be increased to larger than

128 64×

pixels by

means of interpolation, the detection accuracy is still brought down. The DPM

pedestrian detector makes use of a root filter and several part filters to describe the

pedestrians. Information in the pedestrians of high resolution is sufficient.

Figure 2(a) and Figure 2(b) are results obtained by utilizing a standard DPM

Figure 1. The pedestrians with Multiple resolution in a sample picture.

G. D. Zhang et al.

104

(a) (b)

Figure 2. The detection result of standard DPM. (a) The part filter and root filter in DPM

(b) The detection result of standard DPM.

detector to detect pedestrians in

Figure 1. It is obvious that the small-scale pe-

destrians cannot be detected successfully. Therefore, the overall detection effect

can be improved if we can improve the detection effect for low resolution pede-

strians and prevent affecting the detection effect for high resolution pedestrians.

In this paper, we propose a multi-resolution DPM pedestrian detection algo-

rithm, which takes advantage of the standard DPM framework in training the

pedestrian with the resolution factor as a hidden variable. For the high resolu-

tion pedestrians, the response can be figured out in the first place. And its loca-

tion can be estimated with the combination of this high resolution response and

the response under a corresponding low resolution. However, for the low resolu-

tion pedestrians, the judgment over possible locations of these targets is carried

out by only calculating the responses under the low resolution. High resolution

and low resolution are only intuitive concepts in the common sense. In addition,

resolution is closely associated with the heights of pedestrian samples.

Structure of this paper is as follows. In section 2, we thoroughly illustrate the

DPM model for pedestrian detection, depicts the DPM learning algorithm, and

describe the parameter initialization and the training procedures. In section 3,

we illustrate the improved DPM algorithm in case of multi-resolution targets, by

analyzing the features of pedestrian detection under multi-resolution, and de-

scribing the improved multi-resolution DPM pedestrian detection algorithm in

detail. In Section 4, we apply this improved algorithm to a general dataset to

comparatively analyze the experimental results.

2. Overview of Related Theory

2.1. Deformable Part Model

The deformable part model (DPM) consists of a root filter and several part filters

to describe the pedestrians. Specifically, the root filter describes each pedestrian

as a whole, while each part filters describe a part of the pedestrian, such as the

head and hand [9]. In this way, the constructed model can effectively capture the

pedestrian information, and adapt well to the changes of body posture and

dressing of the pedestrian [10].

剩余14页未读，继续阅读

评论收藏

内容反馈

weixin_38651165

粉丝: 4
资源: 901

一种多分辨率行人检测方法的改进

HOG_SVM的行人检测

基于改进Vi Be的多行人检测方法 (2016年)

基于改进WLD描述子的行人检测方法研究

一种改进的YOLO_v3红外图像行人检测方法

一种利用特征选择改进的行人检测模型

一种新的提高CCD成像分辨率的方法

论文研究-一种同时改进声纳量程和分辨率的方法 .pdf

android应用自适应多分辨率的解决方法

一种改进多分辨率图像融合算法.pdf

一种改进的高空间分辨率遥感影像森林类型深度学习精细分类方法：双支FCN-8s.pdf

改进的卷积神经网络行人检测方法.pdf

论文研究-改进的快速行人检测方法.pdf

计算机视觉——行人检测方法改进.pdf

改进hog行人检测源码

改进的卷积神经网络行人检测方法 (2).pdf

一种基于半边折叠的多分辨率模型构造方法.pdf

论文研究-基于周期驱动的动态聚合解聚多分辨率建模仿真方法.pdf

基于YOLOv3模型的实时行人检测改进算法

SAR图像提高分辨率的主要方法间的联系

论文研究-结合多分辨率与块分割的不同聚焦图像融合.pdf

改进的图像超分辨率混合方法

一种基于超分辨率的叠栅条纹图像处理方法

KDE和密度估计方法实现行人检测_行人追踪_matlab

井下视频行人检测方法

论文研究-一种提高流片效率的亚分辨率辅助图形OPC方法 .pdf

一种利用多空间分辨率遥感数据联合反演植被覆盖度的方法

论文研究-基于灰度权重和多分辨率方法的图像匹配加速算法 .pdf

电信设备-一种屏幕分辨率调节方法及移动终端.zip

最新资源