基于混合高斯和HOG+SVM的行人检测模型

doi:10.12066/j.issn.1007-2861.1849

Abstract

Abstract:

To improve detection rate of pedestrian detection system, a novel pedestrian detection model by combining the Gaussian mixture background model and histograms of oriented gradients (HOG) plus support vector machine (SVM) is proposed. First, foreground segmentation is done using the Gaussian mixture model (GMM) to extract moving target areas. In the recognition of pedestrians, the dimension of the HOG descriptor is reduced by resizing the size of the detecting window. In addition, an error recognition region is used to re-update the information of sample dataset to optimize the SVM classifier. Performance of this model is evaluated with the test frames randomly chosen from a video taken in a realistic scene. The results indicate that GMM and HOG+SVM can ensure accuracy and speed of detection, and limit false detection rate to 4 %. Real-time and accurate pedestrian detection in complex scenes are achieved.

Key words: pedestrian detection, Gaussian mixture model (GMM), region extraction, histogram of oriented gradient (HOG), dimension reduction

CLC Number:

TP312

GONG Luming, XU Meihua, LIU Dongjun, ZHANG Fayu. Novel model of pedestrian detection based on Gaussian mixture model and HOG+SVM[J]. Journal of Shanghai University（Natural Science Edition）, 2018, 24(3): 341-351.

Figures/Tables 15

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Table 1

Fig. 14

References 15

[1]	许腾, 黄铁军, 田永鸿. 车载视觉系统中的行人检测技术综述[J]. 中国图象图形学报, 2013,18(4):359-367. doi: 10.11834/jig.20130401
[2]	Sun D, Roth S, Black M J. Secrets of optical flow estimation and their principles [C]// 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2010: 2432-2439.
[3]	张正鹏, 江万寿, 张靖. 光流特征聚类的车载全景序列影像匹配方法[J]. 测绘学报, 2014,43(12):1266-1273.
[4]	杨亚东. 光流法在运动目标识别领域的理论与应用[J]. 电子设计工程, 2013,21(5):24-26.
[5]	严晓明. 一种基于改进帧差法的运动目标检测[J]. 莆田学院学报, 2011,18(5):69-72.
[6]	杨恒, 王超, 姜文涛, 等. 基于随机背景建模的目标检测算法[J]. 应用光学, 2015,36(6):880-887.
[7]	黄大卫, 胡文翔, 吴小培, 等. 改进单高斯模型的视频前景提取与破碎目标合并算法[J]. 信号处理, 2015,31(3):299-307.
[8]	Bouwmans T, El Baf F, Vachon B. Background modeling using mixture of Gaussians for foreground detection: a survey[J]. Recent Patents on Computer Science, 2008,1(3):219-237.
[9]	Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking [C]// 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1999: 2.
[10]	Dalal N, Triggs B. Histograms of oriented gradients for human detection [C]// 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005: 886-893.
[11]	徐超, 高梦珠, 查宇锋, 等. 基于HOG和SVM的公交乘客人流量统计算法[J]. 仪器仪表学报, 2015,36(2):446-452.
[12]	Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995,20(3):273-297.
[13]	郭明玮, 赵宇宙, 项俊平, 等. 基于支持向量机的目标检测算法综述[J]. 控制与决策, 2014,29(2):193-200.
[14]	溪海燕, 肖志涛, 张芳. 基于线性SVM的车辆前方行人检测方法[J]. 天津工业大学学报, 2012,31(1):69-73.
[15]	田仙仙, 鲍泓, 徐成. 一种改进HOG特征的行人检测算法[J]. 计算机科学, 2014,41(9):320-324.

Novel model of pedestrian detection based on Gaussian mixture model and HOG+SVM

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 15

Related Articles 3

Recommended Articles

Metrics

Comments

[1]	Yanwei HU, Meihua XU, Aiying GUO. Fast pedestrian detection algorithm based on BING-casDPM [J]. Journal of Shanghai University（Natural Science Edition）, 2019, 25(5): 712-721.
[2]	LIU Dongjun, XU Meihua, GONG Luming, XIA Chenjun. Feature fusion for front vehicle detection and implementation [J]. Journal of Shanghai University（Natural Science Edition）, 2017, 23(6): 893-.
[3]	HOU Li-Min, MIAO Wei. Prosodic Information Used in Language Identification [J]. Journal of Shanghai University（Natural Science Edition）, 2009, 15(5): 441-444.