Open AccessFast pedestrian detection method based on BING
Author(s) -
Zhao Yong,
Cui Zhongwei,
Zhang Yongjun,
Xu Wenbo,
Liang Hao,
Zuo Yu
Publication year - 2020
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.1160
Subject(s) - pedestrian detection , computer science , artificial intelligence , histogram , sliding window protocol , pyramid (geometry) , constant false alarm rate , computer vision , pattern recognition (psychology) , support vector machine , histogram of oriented gradients , object detection , false positive rate , feature (linguistics) , classifier (uml) , window (computing) , pedestrian , image (mathematics) , mathematics , engineering , linguistics , philosophy , geometry , transport engineering , operating system
Pedestrian detection is one of hot topics in the field of computer vision and pattern recognition, which is of great value to video surveillance, intelligent traffic and human‐computer interaction, etc. and how to improve detection rate and speed is the key. Most traditional pedestrian detection methods are based on the pyramid sliding window scanning method, and for images in which the majority of the region does not contain a body, the detection is inefficient. This study presents a body window sampling algorithm based on binarised normed gradients, which can quickly and effectively extract the window of the image that most likely contains human body to be identified, thus greatly improving the detection speed and obtaining a lower false alarm rate. The authors employ the histogram of oriented gradient feature and the linear support vector machine to train the classifier. For the same false test case in comparison with the pyramid scanning method, when the authors used 2,000 sampling windows for detection, they observed a detection rate increase of 11%, the detection speed increased 19.6 times. For 5,000 sampling windows, the detection rate increased by 20% and the detection speed increased 7.8 times.