A lightweight algorithm for pedestrian detection in overhead images
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00530-024-01365-1.pdf
Reference52 articles.
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2. Ahmed, I., Din, S., Jeon, G., Piccialli, F.: Exploring Deep Learning models for overhead view multiple object detection. IEEE Internet Things J. 7, 5737–5744 (2020). https://doi.org/10.1109/JIOT.2019.2951365
3. Ahmad, M., Ahmed, I., Jeon, G.: An IoT-enabled real-time overhead view person detection system based on Cascade-RCNN and transfer learning. J. Real-Time Image Proc. 18, 1129–1139 (2021). https://doi.org/10.1007/s11554-021-01103-0
4. Kang, J., Tariq, S., Oh, H., Woo, S.S.: A Survey of Deep Learning-based object detection methods and datasets for overhead imagery. IEEE Access. 10, 20118–20134 (2022). https://doi.org/10.1109/ACCESS.2022.3149052
5. Cao, H., Peng, B., Jia, L., Li, B., Knoll, A., Chen, G.: Orientation-aware People Detection and Counting Method based on Overhead Fisheye Camera. In: 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). pp. 1–7 (2022). https://doi.org/10.1109/MFI55806.2022.9913868
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