12,000-fps Multi-object Detection Using HOG Descriptor and SVM Classifier

CORC > 自动化研究所 > 中国科学院自动化研究所 > 精密感知与控制研究中心 > 人工智能与机器学习

	12,000-fps Multi-object Detection Using HOG Descriptor and SVM Classifier
	Li Jianquan1,2 ; Yin Yingjie1,2 ; Liu Xilong1,2 ; Xu De1,2 ; Gu Qingyi 1,2
	2017-09
会议日期	September 24–28, 201
会议地点	Vancouver, BC, Canada
英文摘要	Abstract— This paper describes a high-frame-rate (HFR) vision system that can detect multiple objects in an image of 512×512 pixels at 12,000 frames per seconds (fps). An optimized algorithm is proposed based on conventional Histograms of Oriented Gradient (HOG) descriptor and Support Vector Machine (SVM) classifier algorithms for hardware implementation. By implementing the proposed algorithm on a field-programmable gate array (FPGA) of a high-speed vision platform, multiobject in an image can be detected at 12,000 fps under complex background. In hardware implementation, 64 pixels were processed in parallel with 80 MHz camera clock. Source image and detection results can be transferred to personal computer (PC) in real-time for recording or post-processing. Our developed HFR multi-object detection system was verified by performing several evaluations.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/20064]
专题	精密感知与控制研究中心_人工智能与机器学习
作者单位	1.CASIA 2.UCAS
推荐引用方式 GB/T 7714	Li Jianquan,Yin Yingjie,Liu Xilong,et al. 12,000-fps Multi-object Detection Using HOG Descriptor and SVM Classifier[C]. 见:. Vancouver, BC, Canada. September 24–28, 201.