Pedestrian Perception Method Based on Infrared Stereo Vision

WANG Xiangjun; YANG Shouchang; CHEN Ruixiang

Volume 43 Issue 7

Jul. 2021

Turn off MathJax

Article Contents

Article Navigation > Infrared Technology > 2021 > 43(7): 702-708

WANG Xiangjun, YANG Shouchang, CHEN Ruixiang. Pedestrian Perception Method Based on Infrared Stereo Vision[J]. Infrared Technology , 2021, 43(7): 702-708.

Citation:

WANG Xiangjun, YANG Shouchang, CHEN Ruixiang. Pedestrian Perception Method Based on Infrared Stereo Vision[J]. Infrared Technology , 2021, 43(7): 702-708.

WANG Xiangjun, YANG Shouchang, CHEN Ruixiang. Pedestrian Perception Method Based on Infrared Stereo Vision[J]. Infrared Technology , 2021, 43(7): 702-708.

Citation:

WANG Xiangjun, YANG Shouchang, CHEN Ruixiang. Pedestrian Perception Method Based on Infrared Stereo Vision[J]. Infrared Technology , 2021, 43(7): 702-708.

PDF( 2412 KB)

Pedestrian Perception Method Based on Infrared Stereo Vision

WANG Xiangjun^{1, 2
,},
YANG Shouchang^{1, 2},
CHEN Ruixiang^{1, 2}

1.
State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China
2.
MOEMS Education Ministry Key Laboratory, Tianjin University, Tianjin 300072, China

Received Date: 2020-09-23
Rev Recd Date: 2020-12-09
Publish Date: 2021-07-01

Abstract

Abstract

Based on the thermal infrared characteristics, the infrared stereo vision pedestrian perception method can effectively detect and measure pedestrians in road scenes at night and hazy environments, with the aim of improving driving safety. Owing to less texture details in infrared images, the traditional dense binocular stereo matching algorithm performs poorly. To solve this problem, the region of interest (ROI) is extracted according to the brightness and edge features of the targets in the infrared image. Then, the image feature points are extracted and matched in the ROI to calculate the original sparse depth map. Finally, according to the small depth difference in the surface of the targets, the semi-dense depth map was estimated by combining the ROI and the original depth map. We designed an experimental system to verify the effectiveness of the proposed method. The experimental results showed that the relative error of the depth perception of pedestrians was better than 1.5% at 15 m and 3% at 30 m in the field of view of approximately 120°.
- long-wave infrared,
- stereo vision,
- SURF,
- depth map,
- assisted driving

FullText(HTML)

References(11)

References

[1]	World Health Organization. The Top 10 Causes of Death[DB/OL]. [2018-05-24]https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death.
[2]	Gade R, Moeslund T B. Thermal Cameras and Applications: a Survey[J]. Machine Vision and Applications, 2014, 25(1): 245-262. doi: 10.1007/s00138-013-0570-5
[3]	石永彪, 张湧. 车载红外夜视技术发展研究综述[J]. 红外技术, 2019, 41(6): 504-510. https://www.cnki.com.cn/Article/CJFDTOTAL-HWJS201906002.htm SHI Yongbiao, ZHANG Yong. Survey on development about vehicular infrared night vision technology[J]. Infrared Technology, 2019, 41(6): 504-510. https://www.cnki.com.cn/Article/CJFDTOTAL-HWJS201906002.htm
[4]	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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201304002.htm XU Teng, HUANG Tiejun, TIAN Yonghong. Survey on pedestrian detection technology for on-board vision systems[J]. Journal of Image and Graphics, 2013, 18(4): 359-367. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB201304002.htm
[5]	于博, 马书浩, 李红艳, 等. 远红外车载图像实时行人检测与自适应实例分割[J]. 激光与光电子学进展, 2020, 57(2): 293-303. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202002033.htm YU Bo, MA Shuhao, LI Hongyan, et al. Real-time pedestrian detection for far-infrared vehicle images and adaptive instance segmentation[J]. Laser & Optoelectronics Progress, 2020, 57(2): 293-303. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202002033.htm
[6]	DING M, ZHANG X, CHEN W H, et al. Thermal Infrared Pedestrian Tracking via Fusion of Features in Driving Assistance System of Intelligent Vehicles[J]. Proceedings of the Institution of Mechanical Engineers Part G-Journal of Aerospace Engineering, 2019, 233(16): 6089-6103. doi: 10.1177/0954410019890820
[7]	ZHANG W Y, FU X H, LI W. The Intelligent Vehicle Target Recognition Algorithm Based on Target Infrared Features Combined with Lidar[J]. Computer Communications, 2020, 155: 158-165. doi: 10.1016/j.comcom.2020.03.013
[8]	Richard Hartley, Andrew Zisserman. 计算机视觉中的多视图几何[M]. 韦穗, 章权兵, 译. 北京: 机械工业出版社, 2019: 240-242. Richard Hartley, Andrew Zisserman. Multiple View Geometry in Computer Vision[M]. WEI Sui, ZHANG Quanbing. Beijing: China Machine Press, 2019: 240-242.
[9]	ZHANG Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334. doi: 10.1109/34.888718
[10]	Bertozzi M, Broggi A, Caraffi C, et al. Pedestrian detection by means of far-infrared stereo vision[J]. Computer Vision and Image Understanding, 2006, 106(2): 194-204. doi: 10.1016/j.cviu.2006.07.016
[11]	Bay H, Ess A, Tuytelaars T, et al. SURF: Speeded Up Robust Features[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359. doi: 10.1016/j.cviu.2007.09.014