An improved multi-scale and knowledge distillation method for efficient pedestrian detection in dense scenes-Reference-Cited by-同舟云学术

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An improved multi-scale and knowledge distillation method for efficient pedestrian detection in dense scenes

Published:2024-07-06 Issue:4 Volume:21 Page:
ISSN:1861-8200
Container-title:Journal of Real-Time Image Processing
language:en
Short-container-title:J Real-Time Image Proc

Author:

Xu Yanxiang,Wen Mi,He Wei,Wang Hongwei,Xue Yunsheng

Funder

Foundation for Innovative Research Groups of the National Natural Science Foundation of China

Program of Shanghai Academic Research Leader

Shanghai Science and Technology Commission Project

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11554-024-01507-8.pdf

Reference24 articles.

1. Arshad, H., Khan, M.A., Sharif, M., Yasmin, M., Javed, M.Y.: Multi-level features fusion and selection for human gait recognition: an optimized framework of bayesian model and binomial distribution. Int. J. Mach. Learn. Cybern. 10, 3601–3618 (2019)

2. Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp. 1440–1448 (2015)

3. Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6154–6162 (2018)

4. Wang, X., Xiao, T., Jiang, Y., Shao, S., Sun, J., Shen, C.: Repulsion loss: detecting pedestrians in a crowd. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7774–7783 (2018)

5. Deng, C., Wang, M., Liu, L., Liu, Y., Jiang, Y.: Extended feature pyramid network for small object detection. IEEE Trans. Multimed. 24, 1968–1979 (2021)

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