Abstract
Multivariate data acquisition is a difficult problem in traceability of planting industry. Video-based human behaviour recognition technology can automatically identify various human behaviors in the process of crops planting, and realize automatic data collection. A feature extraction method based on three-dimensional skeleton of human body and an improved SVM_KNN method has been proposed in this paper to classify human behavior and realize multi-target human behavior recognition based on video. The experiment results show that the human behavior recognition method proposed in this paper can effectively identify different human behaviors in crop planting.
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References
Wang, S.G., Lu, F.J.: Action recognition based on line random forest voting. Opt. Precis. Eng. 24(08), 2010–2017 (2016)
Bobick, A., Davis, A.: The recognition of human movement using temporal templates. IEEE trans. Pattern anal. Mach. Intell. 23(3), 257–267 (2001)
Weinland, D., Ronfard, D., Boyer, D.: A survey of vision-based methods for action representation, segmentation and recognition. Comput. Vis. Image Underst. 115(2), 224–241 (2011)
Wang, H., HKlaser, A.: Action recognition by dense trajectories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 7 (2011)
Song, R., Lin, Z.X., Yao, M.: Luminance uniformity evaluation for backlight based on ε–support vector regression. Chin. J. Liquid Cryst. Displays. 30(05), 857–863 (2015)
Shen, L., Chen, H., Yu, Z.: Evolving support vector machines using fruit fly optimization for medical data classification. Knowl.-Based Syst. 96, 61–75 (2016)
Huang, C.L., Wang, C.J.: A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Appl. 31(2), 231–240 (2006)
Rogez, G., Rihan, J., Ramalingam, S., et al.: Randomized trees for human pose detection. In: 2008 International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1032–1039 (2008)
Ghojogh, B., Mohammadzade, H., Mokari, M.: Fisherposes for human action recognition using kinect sensor data. IEEE Sens. J. 18(4), 1612–1627 (2018)
Knoop, S., Vacek, S., Dillmann, R.: Sensor fusion for 3D human body tracking with an articulated 3D body model. In: 2006 International Conference on Robotics and Automation (ICRA), pp. 1686–1691 (2006)
Acknowledgements
This work was financially supported by Hunan science and technology project (No. 2016NK2211) and scientific research projects of Hunan education department (No. 17C0480).
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Ni, W., Rao, Q., Luo, D. (2019). Video Human Behaviour Recognition Based on Improved SVM_KNN for Traceability of Planting Industry. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_52
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DOI: https://doi.org/10.1007/978-3-030-31456-9_52
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