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Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 – December 1, 2019, Proceedings, Part II

Research Article

A Panoramic Video Face Detection System Design and Implement

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  • @INPROCEEDINGS{10.1007/978-3-030-41117-6_8,
        author={Hang Zhao and Dian Liu and Bin Tan and Songyuan Zhao and Jun Wu and Rui Wang},
        title={A Panoramic Video Face Detection System Design and Implement},
        proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part II},
        proceedings_a={CHINACOM PART 2},
        year={2020},
        month={2},
        keywords={Panorama Face detection SURF CUDA},
        doi={10.1007/978-3-030-41117-6_8}
    }
    
  • Hang Zhao
    Dian Liu
    Bin Tan
    Songyuan Zhao
    Jun Wu
    Rui Wang
    Year: 2020
    A Panoramic Video Face Detection System Design and Implement
    CHINACOM PART 2
    Springer
    DOI: 10.1007/978-3-030-41117-6_8
Hang Zhao1, Dian Liu1, Bin Tan2, Songyuan Zhao1, Jun Wu1,*, Rui Wang1
  • 1: Tongji University, No. 4800 Caoan Road
  • 2: College of Electronics and Information Engineering
*Contact email: wujun@tongji.edu.cn

Abstract

A panorama is a wide-angle view picture with high-resolution, usually composed of multiple images, and has a wide range of applications in surveillance and entertainment. This paper presents a end-to-end real-time panoramic face detection video system, which generates panorama video efficiently and effectively with the ability of face detection. We fix the relative position of the camera and use the speeded up robust features (SURF) matching algorithm to calibrate the cameras in the offline stage. In the online stage, we improve the parallel execution speed of image stitching using the latest compute unified device architecture (CUDA) technology. The proposed design fulfils high-quality automatic image stitching algorithm to provide a seamless panoramic image with 6k resolution at 25 fps. We also design a convolutional neural network to build a face detection model suitable for panorama input. The model performs very well especially in small faces and multi-faces, and can maintain the detection speed of 25 fps at high resolution.

Keywords
Panorama Face detection SURF CUDA
Published
2020-02-27
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-41117-6_8
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