Pedestrian detection with vehicle-mounted far-infrared monocular sensor based on edge segmentation

被引：1

作者：

Liu, Qiong ^{[1
]}

Wang, Guo-Hua ^{[1
]}

Shen, Min-Min ^{[1
]}

机构：

[1] School of Software Engineering, South China University of Technology, Guangzhou, 510006, Guangdong

来源：

Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2015年 / 43卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Driving assistance system; Far-infrared pedestrian detection; Head recognition; Histogram of oriented gradient; Sobel segmentation; Support vector machine;

D O I：

10.3969/j.issn.1000-565X.2015.01.014

中图分类号：

学科分类号：

摘要：

As the pedestrian detection with vehicle-mounted far-infrared monocular sensor using machine learning is usually poor in real-time performance and precision, a head-histogram of oriented gradient-support vector machine (Head-HOG-SVM) approach based on edge segmentation is proposed. The weighted Sobel operator is adopted to enhance the vertical edges of pedestrians in the regions of interest (ROIs). Several pedestrian detection methods are selected according to the pedestrian appearance in different distance. A head feature is used to detect pedestrians at near and middle distance to improve the real-time performance of the system, and a HOG-SVM classifier cascading with head recognition is used to detect blurred pedestrians at far distance. Experimental results on the several videos captured from suburb scenes show that, in comparison with the HOG-SVM classifier based on dual threshold segmentation, the precision and detection rate of the proposed method are respectively increased by 33% and 200%. ©, 2015, South China University of Technology. All right reserved.

引用

页码：87 / 91and98

页数：9111

共 13 条

[1]

Dollar P., Wojek C., Schiele B., Et al., Pedestrian detection: an evaluation of the state of the art, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 4, pp. 743-761, (2012)

[2]

Sun H., Wang C., Wang B., Et al., Pyramid binary pattern features for real-time pedestrian detection from infrared videos, Neurocomputing, 74, 5, pp. 797-804, (2011)

[3]

Zhuang J.-J., Liu Q., Nighttime pedestrian detection method for driver assistance systems, Journal of South China University of Technology: Natural Science Edition, 40, 8, pp. 56-62, (2012)

[4]

Yan J., Zhang X., Lei Z., Et al., Robust multi-resolution pedestrian detection in traffic scenes, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3033-3040, (2013)

[5]

Ge J., Luo Y., Tei G., Real-time pedestrian detection and tracking at nighttime for driver-assistance systems, IEEE Transactions on Intelligent Transportation Systems, 10, 2, pp. 283-298, (2009)

[6]

Bertozzi M., Broggi A., Felisa M., Et al., Low-level pedestrian detection by means of visible and far infra-red terra-vision C], Proceedings of Intelligent Vehicles Symposium, pp. 231-236, (2006)

[7]

Liu Q., Zhuang J.J., Ma J., Robust and fast pedestrian detection method for far-infrared automotive driving assistance systems, Infrared Physics and Technology, 60, pp. 288-299, (2013)

[8]

Zin T.T., Tin P., Hama H., Bundling multislit-HOG features of near infrared images for pedestrian detection, Proceedings of the 4th International Conference on Innovative Computing, Information and Control, pp. 302-305, (2009)

[9]

Dalai N., Triggs B., Histograms of oriented gradients for human detection, Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886-893, (2005)

[10]

Olmeda D., de la Escalera A., Armingol J.M., Detection and tracking of pedestrians in infrared images, Proceedings of the 3rd International Conference on Signals, Circuits and Systems, pp. 1-6, (2009)

← 1 2 →