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

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 精密感知与控制

	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 Qingyi1,2
	2017-09
会议名称	IEEE/RSJ International Conference on Intelligent Robots and Systems
会议日期	September 24–28, 201
会议地点	Vancouver, BC, Canada
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.
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20064
专题	中科院工业视觉智能装备工程实验室_精密感知与控制中国科学院自动化研究所
作者单位	1.中国科学院自动化研究所 2.中国科学院大学
第一作者单位	中国科学院自动化研究所
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],2017.

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文件名称/大小	文献类型	版本类型	开放类型	使用许可
12,000-fps Multi-obj（1422KB）	会议论文		开放获取	CC BY-NC-SA	浏览下载