VLSI Implementation of an Adaptive Block Partition Decision Object-Detection Design for Real-Time 4K2K Video Display

被引:1
作者
Chen, Shih-Lun [1 ]
Tuan, Min-Chun [1 ]
机构
[1] Chung Yuan Christian Univ, Dept Elect Engn, Taoyuan 32023, Taiwan
来源
JOURNAL OF DISPLAY TECHNOLOGY | 2016年 / 12卷 / 12期
关键词
Application-specific integrated circuits (ASICs); block partition; edge detection; FPGA; image processing; object detection; very large-scale integration (VLSI); ALGORITHM; FEATURES;
D O I
10.1109/JDT.2016.2611617
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, an adaptive block partition decision methodology is presented for very large-scale integration (VLSI) implementation of object-detection for real-time ultrahigh-definition (4K2K) resolution video displaying. The proposed adaptive block partition decision algorithm includes a data controller, a gray-level generator, a subblock difference module, and an edge detector. The edge detector is designed for discovering edges in images using an efficient edge-catching technique. An adaptive block partition decision technique was added to enhance the shapes of objects and to decrease the edge distortion effects. Furthermore, a threshold constraint is used to set parameters for different sizes of blocks. A statistic methodology of object detection is also used to determine whether it is necessary to trigger an alert signal or not. The VLSI architecture of the proposed design contains 6.99-K gate counts. Its power consumption is 1.63 mW and its operating frequency is to 374.5 MHz by using a 90-nm CMOS technology. Compared with previous designs, the proposed design not only achieves reduction of more silicon area, but also increases the processing throughput, and accuracy of object-detection for real-time video display.
引用
收藏
页码:1570 / 1580
页数:11
相关论文
共 38 条
  • [31] Toyama K., 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision, P255, DOI 10.1109/ICCV.1999.791228
  • [32] Algorithm and Architecture Design of Human-Machine Interaction in Foreground Object Detection With Dynamic Scene
    Tsai, Tsung Han
    Lin, Chung-Yuan
    Li, Sz-Yan
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2013, 23 (01) : 15 - 29
  • [33] Foreground object detection based on multi-model background maintenance
    Tsai, Tsung-Han
    Sheu, Wen-Tsai
    Lin, Chung-Yuan
    [J]. ISM WORKSHOPS 2007: NINTH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA - WORKSHOPS, PROCEEDINGS, 2007, : 151 - 158
  • [34] Tuan MC, 2015, 2015 IEEE/ACIS 14TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS), P149, DOI 10.1109/ICIS.2015.7166585
  • [35] New Technique for Embedding Depth Information in Captured Images Using Light Beam Containing Invisible High-Frequency Patterns-Design and Preparation of New Experimental Setup With Some Comments
    Unno, Hiroshi
    Isaka, Sae
    Takashima, Youichi
    Uehira, Kazutake
    [J]. JOURNAL OF DISPLAY TECHNOLOGY, 2015, 11 (02): : 136 - 145
  • [36] Robust real-time face detection
    Viola, P
    Jones, MJ
    [J]. INTERNATIONAL JOURNAL OF COMPUTER VISION, 2004, 57 (02) : 137 - 154
  • [37] Video Surveillance Over Wireless Sensor and Actuator Networks Using Active Cameras
    Wu, Dalei
    Ci, Song
    Luo, Haiyan
    Ye, Yun
    Wang, Haohong
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2011, 56 (10) : 2467 - +
  • [38] Zatt B, 2011, DES AUT CON, P1026