Open Access
| Issue |
MATEC Web Conf.
Volume 355, 2022
2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
|
|
|---|---|---|
| Article Number | 03020 | |
| Number of page(s) | 11 | |
| Section | Computing Methods and Computer Application | |
| DOI | https://doi.org/10.1051/matecconf/202235503020 | |
| Published online | 12 January 2022 | |
- Dalal N, Triggs B. Histograms of oriented radients for human detection[C]//IEEE Conference of Computer Vision and Pattern Recognition, 2005, 1:886-893. [Google Scholar]
- Viola Paul, Jones M J. Robust teal-time face detection[J]. Journal of Computer Vision, 2004,57(2):137-154. [CrossRef] [Google Scholar]
- Dollár Piotr, Wojek Christian, Schiele Bernt, et al. Pedestrian detection:an evaluation of the state of the art[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 34: 743-61(10.1109/ TPAMI.2011.155). [Google Scholar]
- CHEN P H, LIN C J, Schölkopf B. A tutorial on v-support vector machines[J]. Appl. Stoch. Models. Bus. Ind., 2005, 21,: 111-136. [CrossRef] [Google Scholar]
- Y Freund, R E Schapire. Adecision-theoretic generalization of on-line learning and an application to boosting[J].Journal of Computer and SystemSciences, 1997, 55(1): 119-139. [CrossRef] [Google Scholar]
- Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]. neural information processing systems, 2015: 91-99. [Google Scholar]
- LIU W, Anguelov D, et al. SSD: single shot multibox detector[C]//Proceedings of European Conference on Computer Vision, 2016: 21-37. [Google Scholar]
- Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of CVPR, 2015: 779-788. [Google Scholar]
- Ge J, Luo Y, Tei G. Real-Time Pedestrian Detection and Tracking at Nighttime for Driver-Assistance Systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(2): 283-298. [CrossRef] [Google Scholar]
- Wu D, Lv S, Jiang M, et al. Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection f apple flowers in natural environments[J]. Computers and Electronics in Agriculture,2020,178(5):174-178. [Google Scholar]
- BOTTOU L. Stochastic gradient descent tricks[M]. Neural Networks: Tricks of the Trade. 2nd ed. Berlin Germany: Springer, 2012: 421–436. doi: 10.1007/978-3-642-35289-8_25. [CrossRef] [Google Scholar]
- Navaneeth Bodla, Bharat Singh, Rama Chellappa Larry, S. Davis. Improving Object Detection With One Line of Code[C]. arXiv preprint arXiv:1704.04503, 2017. [Google Scholar]
- Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, Jian Sun. YOLOX: Exceeding YOLO Series in 2021[J]. arXiv preprint arXiv:2107.08430, 2021. [Google Scholar]
- REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas: IEEE, 2016: 779-788. [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.