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Research on Evaluation Technology of Police Robot Video and Image Application

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Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1088))

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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.

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References

  1. Fischler, M. 1973. The representation and matching of pictorial structures. IEEE Transactions on Computers, 22 (1): 67–92.

    Article  Google Scholar 

  2. 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.

    Google Scholar 

  3. 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.

    Article  Google Scholar 

  4. Jia, X., G. Zhu. 2017. Joint face detection and facial expression recognition with MTCNN. International Conference on Information Science & Control Engineering.

    Google Scholar 

  5. 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.

    Google Scholar 

  6. Najibi, M., P. Samangouei, R. Chellappa, et al. 2017. SSH: Single stage headless face detector. IEEE International Conference on Computer Vision, 4885–4894.

    Google Scholar 

  7. 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.

    Google Scholar 

  8. 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.

    Article  Google Scholar 

  9. Dai haineng, MAO yaobin. 2018. An improved face detection algorithm based on r-fcn model. Computer and modernization, 276(8): 16–19+24.

    Google Scholar 

  10. 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.

    Google Scholar 

  11. Liu, W., D. Anguelov, D. Erhan, et al. 2016. SSD: Single Shot MultiBox Detector. European Conference on Computer Vision, 21–37.

    Google Scholar 

  12. Redmon, J., A. Farhadi. 2017. YOLO9000: Better, Faster, Stronger. IEEE Conference on Computer Vision & Pattern Recognition, 6517–6525.

    Google Scholar 

  13. 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.

    Article  Google Scholar 

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Acknowledgements

Our research was sponsored by National Key R&D Program of China (No. 2017YFC0806500).

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Correspondence to Ming Yang .

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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

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