CASIA OpenIR  > 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 Qingyi1,2
2017-09
Conference NameIEEE/RSJ International Conference on Intelligent Robots and Systems
Conference DateSeptember 24–28, 201
Conference PlaceVancouver, BC, Canada
AbstractAbstract— 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.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20064
Collection精密感知与控制研究中心_人工智能与机器学习
Affiliation1.CASIA
2.UCAS
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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