Open Access
| Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
|---|---|---|
| Article Number | 00061 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/bioconf/20249700061 | |
| Published online | 05 April 2024 | |
- T. N. Mundhenk, G. Konjevod, W. A. Sakla, and K. Boakye, “A large contextual dataset for classification, detection and counting of cars with deep learning,” in Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14, Springer, 2016, pp. 785–800. [CrossRef] [Google Scholar]
- V. Lempitsky and A. Zisserman, “Learning To Count Objects in Images.” [Google Scholar]
- Z. Ma, L. Yu, and A. B. Chan, “Small Instance Detection by Integer Programming on Object Density Maps.” [Google Scholar]
- Z. Ma, X. Wei, X. Hong, and Y. Gong, “Bayesian Loss for Crowd Count Estimation with Point Supervision.” [Online]. Available: https://github.com/ZhihengCV/ [Google Scholar]
- A. Zhang et al., “Relational Attention Network for Crowd Counting.” [Google Scholar]
- A. A. Hashim, M. M. Rasheed, and S. A. Abdullah, “ANALYSIS OF BLUETOOTH LOW ENERGYBASED INDOOR LOCALIZATION SYSTEM USING MACHINE LEARNING ALGORITHMS.” [Google Scholar]
- A. Kumar Suhane, A. Vani, and U. Raghuwanshi, “HUMAN DETECTION AND CROWD COUNTING USING YOLO.” [Online]. Available: https://www.researchgate.net/publication/370341591 [Google Scholar]
- H. Gomes, N. Redinha, N. Lavado, and M. Mendes, “Counting People and Bicycles in Real Time Using YOLO on Jetson Nano,” Energies (Basel), 15, no. 23, Dec. 2022, DOI: 10.3390/en15238816. [Google Scholar]
- Y. Fang, B. Zhan, W. Cai, S. Gao, and B. Hu, “Locality-constrained spatial transformer network for video crowd counting,” in Proceedings - IEEE International Conference on Multimedia and Expo, IEEE Computer Society, Jul. 2019, pp. 814–819. DOI: 10.1109/ICME.2019.00145. [Google Scholar]
- J. Gao, Q. Wang, and X. Li, “PCC Net: Perspective crowd counting via spatial convolutional network,” IEEE Transactions on Circuits and Systems for Video Technology, 30, no. 10, pp. 3486–3498, Oct. 2020, DOI: 10.1109/TCSVT.2019.2919139. [CrossRef] [Google Scholar]
- X. Jiang et al., “Attention Scaling for Crowd Counting.” [Google Scholar]
- W. Liu, M. Salzmann, and P. Fua, “Context-Aware Crowd Counting.” [Online]. Available: https://sites.google.com/view/weizheliu/home/ [Google Scholar]
- L. Liu, Z. Qiu, G. Li, S. Liu, W. Ouyang, and L. Lin, “Crowd Counting with Deep Structured Scale Integration Network.” [Google Scholar]
- M. Shi, Z. Yang, C. Xu, and Q. Chen, “Revisiting Perspective Information for Efficient Crowd Counting.” [Google Scholar]
- Q. Wang, J. Gao, W. Lin, and Y. Yuan, “Learning from Synthetic Data for Crowd Counting in the Wild.” [Online]. Available: www.youtube.com/watch?v=Hvl7xWkIueo. [Google Scholar]
- Q. Wang, J. Gao, W. Lin, and X. Li, “NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization,” IEEE Trans Pattern Anal Mach Intell, 43, no. 6, pp. 2141–2149, Jun. 2021, DOI: 10.1109/TPAMI.2020.3013269. [CrossRef] [PubMed] [Google Scholar]
- Z. Yan et al., “Perspective-Guided Convolution Networks for Crowd Counting.” [Google Scholar]
- A. Dalwadi et al., “Detecting and Counting People In Dense Crowd,” 2012. [Online]. Available: www.ijfans.org [Google Scholar]
- M. Gochoo et al., “FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection.” [Online]. Available: https://github.com/MoyoG/FishEye8K [Google Scholar]
- M. Naphade et al., “The 7th AI City Challenge.” [Google Scholar]
- J. Terven and D. Cordova-Esparza, “A Comprehensive Review of YOLO: From YOLOv1 and Beyond,” Apr. 2023, [Online]. Available: http://arxiv.org/abs/2304.00501 [Google Scholar]
- S. Manzoor, Y. C. An, G. G. In, Y. Zhang, S. Kim, and T. Y. Kuc, “SPT: Single Pedestrian Tracking Framework with Re-Identification-Based Learning Using the Siamese Model,” Sensors, 23, no. 10, May 2023, DOI: 10.3390/s23104906. [Google Scholar]
- X. Li et al., “Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection.” [Google Scholar]
- Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye, and D. Ren, “Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression,” 2016. [Online]. Available: https://github.com/Zzh-tju/DIoU. [Google Scholar]
- M. H. Putra, Z. M. Yussof, K. C. Lim, and S. I. Salim, “Convolutional Neural Network for Person and Car Detection using YOLO Framework”. [Google Scholar]
- S. Gothane, “A Practice for Object Detection Using YOLO Algorithm,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, pp. 268–272, Apr. 2021, DOI: 10.32628/cseit217249. [CrossRef] [Google Scholar]
- L. Qi et al., “Ship target detection algorithm based on improved faster R-CNN,” Electronics (Switzerland), 8, no. 9, Sep. 2019, DOI: 10.3390/electronics8090959. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.