AFC-Net: adjacent feature complementary for crowded pedestrian detection-Reference-Cited by-同舟云学术

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AFC-Net: adjacent feature complementary for crowded pedestrian detection

Published:2023-08-18 Issue:5 Volume:34 Page:
ISSN:0932-8092
Container-title:Machine Vision and Applications
language:en
Short-container-title:Machine Vision and Applications

Author:

Wang Jing^ORCID,Zhao Cailing,Liu Zhiqiang,Huo Zhanqiang^ORCID

Funder

Fundamental Research Funds for the Universities of Henan Province

Excellent Young Teachers Program of Henan Polytechnic University

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://link.springer.com/content/pdf/10.1007/s00138-023-01439-6.pdf

Reference42 articles.

1. Bi, L., Feng, D.D., Fulham, M., Kim, J.: Multi-label classification of multi-modality skin lesion via hyper-connected convolutional neural network. Pattern Recogn. 107, 107502 (2020). https://doi.org/10.1016/j.patcog.2020.107502

2. Ke, X., Lin, X., Qin, L.: Lightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios. Mach. Vis. Appl. (2021). https://doi.org/10.1007/s00138-021-01169-7

3. Murthy, C.B., Hashmi, M.F., Keskar, A.: Efficientlitedet: a real-time pedestrian and vehicle detection algorithm. Mach. Vis. Appl. (2022). https://doi.org/10.1007/s00138-022-01293-y

4. Jung, H.-K., Choi, G.-S.: Improved yolov5: efficient object detection using drone images under various conditions. Appl. Sci. (2022). https://doi.org/10.3390/app12147255

5. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94

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