| Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 4 | |
| Section | Network Security System, Neural Network and Data Information | |
| DOI | https://doi.org/10.1051/matecconf/201823201003 | |
| Published online | 19 November 2018 | |
Complexity and accuracy analysis of common artificial neural networks on pedestrian detection
School of Data and Computer Science, Sun Yat-sen University (SYSU), Guangzhou, 510006, P. R. China
* Corresponding author: wujt9@mail2.sysu.edu.cn
With the development of computer version, deep learning and artificial neural networks approaches like SPP-net, Faster-RCNN and YOLO are proposed. This paper compares them in terms of efficiency and effectiveness. By analyzing the network architecture, SPP-net is more complex than Faster-RCNN and YOLO. By analyzing the experiments, SPP-net and Faster-RCNN are more accurate than YOLO in static detection while opposite in real-time system. Therefore, in real-time pedestrian detection situation, YOLO can perform better. In static pedestrian detection situation, Faster-RCNN or SPP-net can perform better.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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