SCoPE: Towards a Systolic Array for SVM Object Detection

被引:24
|
作者
Kyrkou, Christos [1 ]
Theocharides, Theocharis [1 ]
机构
[1] Univ Cyprus, Dept Elect & Comp Engn, Nicosia, Cyprus
关键词
Field-programmable gate array (FPGA); object detection; support vector machine (SVM); systolic array;
D O I
10.1109/LES.2009.2034709
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents SCoPE (Systolic Chain of Processing Elements), a first step towards the realization of a generic systolic array for support vector machine (SVM) object classification in embedded image and video applications. SCoPE provides efficient memory management, reduced complexity, and efficient data transfer mechanisms. The proposed architecture is generic and scalable, as the size of the chain, and the kernel module can be changed in a plug and play approach without affecting the overall system architecture. These advantages provide versatility, scalability and reduced complexity that make it ideal for embedded applications. Furthermore, the SCoPE architecture is intended to be used as a building block towards larger systolic systems for multi-input or multi-class classification. Simulation results indicate real-time performance, achieving face detection at similar to 33 frames per second on an FPGA prototype.
引用
收藏
页码:46 / 49
页数:4
相关论文
共 50 条
  • [1] Cascade Linear SVM for Object Detection
    Song, Jinze
    Wu, Tao
    An, Ping
    PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE FOR YOUNG COMPUTER SCIENTISTS, VOLS 1-5, 2008, : 1755 - 1759
  • [2] A Systolic Array Architecture for SVM Classifier for Machine Learning on Embedded Devices
    Ramadurgam, Srikanth
    Perera, Darshika G.
    2023 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS, 2023,
  • [3] A cascaded mixture SVM classifier for object detection
    Yuan, ZJ
    Zheng, N
    Liu, YH
    ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 1, PROCEEDINGS, 2005, 3496 : 906 - 912
  • [4] Review of object detection methods based on SVM
    Chen, Z.-H. (chenzh@ustc.edu.cn), 1600, Northeast University (29):
  • [5] Realization of Systolic Array design for Earthquake Detection
    Abbas, S. Syed Ameer
    Jeyaraj, G. Kumaresar
    Ramanan, M. J. V.
    2017 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN DATA SCIENCE (ICCIDS), 2017,
  • [6] Towards Dependable Object Detection
    Selvaraj, Nithish Muthuchamy
    Muhammad, Ilyas
    Cheah, Chien Chern
    IECON 2020: THE 46TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2020, : 523 - 528
  • [7] SVM-based Approach for Buried Object Detection
    Zhang, Qing He
    Yao, Jing-Jing
    PIERS 2010 XI'AN: PROGRESS IN ELECTROMAGNETICS RESEARCH SYMPOSIUM PROCEEDINGS, VOLS 1 AND 2, 2010, : 1657 - +
  • [8] Drift Detection Using SVM in Structured Object Tracking
    Ratnayake, Kumara
    Amer, Maria A.
    IMAGE ANALYSIS AND RECOGNITION, ICIAR 2019, PT I, 2019, 11662 : 67 - 76
  • [9] Regional SVM Classifiers with a Spatial Model for Object Detection
    Teng, Zhu
    Zhang, Baopeng
    Kim, Onecue
    Kang, Dong-Joong
    PROCEEDINGS OF THE 2014 9TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, THEORY AND APPLICATIONS (VISAPP 2014), VOL 2, 2014, : 372 - 379
  • [10] Convolutional SVM Networks for Object Detection in UAV Imagery
    Bazi, Yakoub
    Melgani, Farid
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2018, 56 (06): : 3107 - 3118