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.
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Acknowledgment
The authors thank the editors and the anonymous reviewers for their invaluable comments to help to improve the quality of this paper. This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61762053, 61831018, 61571329, Guangdong Province Key Research and Development Program Major Science and Technology Projects under Grant 2018B010115002, Key Laboratory of Embedded System and Service Computing (Tongji University), Ministry of Education, Shanghai (ESSCKF 2018-06).
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zhao, H., Liu, D., Tan, B., Zhao, S., Wu, J., Wang, R. (2020). A Panoramic Video Face Detection System Design and Implement. In: Gao, H., Feng, Z., Yu, J., Wu, J. (eds) Communications and Networking. ChinaCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-030-41117-6_8
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