Abstract
Nowadays, intelligent technology is more and more widely used, especially in video and image area. The quality of the algorithm or model, as well as the adaptability to the application directly affects the output of the application software. Research institutions and development enterprises can find the flaws of their own technology through comprehensive evaluation. They can also find valuable research direction by observing the comprehensive performance evaluation results on the system platform and seeking technological innovation, thus promoting the overall progress of intelligent video application technology. The purpose of algorithm and system performance evaluation is to find the valuable direction by comparing the performance difference between algorithms and evaluating the level of detection or recognition technology. This paper proposes a series of evaluation methods and indexes for two kinds of intelligent video applications: face detection and recognition, object detection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fischler, M. 1973. The representation and matching of pictorial structures. IEEE Transactions on Computers, 22 (1): 67–92.
Wu, Jianxin, S. Charles Brubaker, Matthew D. Mullin, et al. 2008. Fast asymmetric learning for cascade face detection. IEEE Transactions on Pattern Analysis & Machine Intelligence, 30(3): 369–382.
Yang, S., P. Luo, C. C. Loy, et al. 2018. Faceness-net: Face detection through deep facial part Responses. IEEE Transactions on Pattern Analysis & Machine Intelligence, 40(8): 1845–1859.
Jia, X., G. Zhu. 2017. Joint face detection and facial expression recognition with MTCNN. International Conference on Information Science & Control Engineering.
Qin, Xiaoran, Yafeng Zhou, Zheqi He, et al. 2017. A faster R-CNN based method for comic characters face detection 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE Computer Society, 1074–1080.
Najibi, M., P. Samangouei, R. Chellappa, et al. 2017. SSH: Single stage headless face detector. IEEE International Conference on Computer Vision, 4885–4894.
Tang, Xu, Daniel K. Du, Zeqiang He, and Jingtuo Liu. 2018. PyramidBox: A context-assisted single shot face detector. The European Conference on Computer Vision, 812–828.
Ren, Shaoqing, Kaiming He, Ross Girshick, et al. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39(6): 1137–1149.
Dai haineng, MAO yaobin. 2018. An improved face detection algorithm based on r-fcn model. Computer and modernization, 276(8): 16–19+24.
He, Kaiming, Georgia Gkioxari, Piotr Dollar, et al. 2017. Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society, 2980–2988.
Liu, W., D. Anguelov, D. Erhan, et al. 2016. SSD: Single Shot MultiBox Detector. European Conference on Computer Vision, 21–37.
Redmon, J., A. Farhadi. 2017. YOLO9000: Better, Faster, Stronger. IEEE Conference on Computer Vision & Pattern Recognition, 6517–6525.
Buckland, Michael K., Fredric C. Gey. 1994. The Relationship between recall and precision. Journal of the Association for Information Science & Technology, 45(1): 12–19.
Acknowledgements
Our research was sponsored by National Key R&D Program of China (No. 2017YFC0806500).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, M., Liu, N. (2020). Research on Evaluation Technology of Police Robot Video and Image Application. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_234
Download citation
DOI: https://doi.org/10.1007/978-981-15-1468-5_234
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1467-8
Online ISBN: 978-981-15-1468-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)