Abstract
Human behavior recognition has become one of the most active topics in computer vision and pattern recognition, which has a wide range of promising applications. In order to overcome the deficiency of single representation feature, a new recognition algorithm of human behavior based on multi-feature fusion of image and conditional random fields (CRF) is presented in this paper. The proposed algorithm consists of three essential cascade modules. First, AE features and RNN features were obtained by extracting the behaviors of the action by the recurrent neural network (RNN) and the AutoEncoder (AE), Then, feature similarity was introduced, the AE features and RNN features were fused to form a more comprehensive and accurate AE-RNN feature by using feature similarity. Finally, the multiple features were using for recognizing the human behavior of image by conditional random fields. The experimental results show that the proposed algorithm is effective and promising and has higher accurate recognition rate which can adapt to complex background and behavioral changes.
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References
Candamo, J., Shreve, M., Goldgof, D.B., et al.: Understanding transit scenes: a survey on human behavior-recognition algorithms. IEEE Trans. Intell. Transp. Syst. 11(1), 206–224 (2010)
Xia, L.-M., Wang, Q., Wu, L.-S.: Vision based behavior prediction of ball carrier in basketball matches. J. Cent. South Univ. 19(8), 2142–2151 (2012)
Feng, Z., Yang, B., Li, Y.: Realtime oriented behavior driven 3D freehand tracking for direct interaction. Pattern Recogn. 46(2), 590–608 (2013)
Wu, D., Shao, L.: Silhouette analysis-based action recognition via exploiting human poses. IEEE Trans. Circuits Syst. Video Technol. 23(2), 236–243 (2013)
Derpanis, K.G., Sizintsev, M., Cannons, K.J.: Action spotting and recognition based on a spatiotemporal orientation analysis. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 527–540 (2013)
Lin, S., Wu, Y., Yu, F., et al.: Posture sequence finite-state machine method for motion recognition. J. Comput. Aided Des. Comput. Graph. 26(9), 1403–1411 (2014)
Shao, Y.-H., Guo, Y.-C., Gao, C.: Human action recognition using multi-feature fusion. J. Optoelectron. Laser 25(9), 1818–1823 (2014)
Morton, J., Wheeler, T.A., Kochenderfer, M.J.: Analysis of recurrent neural networks for probabilistic modeling of driver behavior. IEEE Trans. Intell. Transp. Syst. 99, 1–10 (2017)
Richard, A., Gall, J.: A bag-of-words equivalent recurrent neural network for action recognition. Comput. Vis. Image Underst. 156, 79–91 (2017)
Ijjina, E.P., Krishna, M.C.: Classification of human actions using pose-based features and stacked auto encoder. Pattern Recogn. Lett. 83, 268–277 (2016)
Gao, S., Zhang, Y., Jia, K., et al.: Single sample face recognition via learning deep supervised autoencoders. IEEE Trans. Inf. Forensics Secur. 10(10), 2108–2118 (2015)
Chen, Z.-H., Lan, Y.-Y., Guo, J.-F.: Distributed stochastic gradient descent with discriminative aggregating. Chinese J. Comput. 38(10), 2054–2063 (2015)
Mori, F., Yamada, H., Mizuno, M., et al.: Color image segmentation based on statistics of location and feature similarity. IEEJ Trans. Electron. Inf. Syst. 131(11), 2022–2029 (2010)
Pereira, S., Pinto, A., Oliveira, J., et al.: Automatic brain tissue segmentation in MR images using random forests and conditional random fields. J. Neurosci. Methods 270, 111–123 (2016)
Zhang, C.Y., Hong, X.G., Peng, Z.H.: Extracting Web entity activities based on SVM and extended conditional random fields. J. Softw. 23(10), 2612–2627 (2014)
Batchuluun, G., Kim, J.H., Hong, H.G., et al.: Fuzzy system based human behavior recognition by combining behavior prediction and recognition. Expert Syst. Appl. 81(C), 108–133 (2017)
Yao, B., Hagras, H., Alhaddad, M.J., et al.: A fuzzy logic-based system for the automation of human behavior recognition using machine vision in intelligent environments. Soft. Comput. 19(2), 499–506 (2015)
Acknowledgements
This work was financially supported by the Key Technology Projects of Henan province of China under Grant 15210241004, Supported by Program for Changjiang Scholars and Innovative Research Team in University, the Key Technology Projects of Henan Educational Department of China under Grant 16A520036, the Key Technology Projects of Henan Educational Department of China under Grant 16B520001, the National Natural Science Foundation of China under Grant 41001251, Anyang science and technology plan project:Researches on Road Extraction Algorithm based on MRF for High Resolution Remote Sensing Image, and the Research and Cultivation Fund Project of Anyang Normal University under Grant AYNU-KP-B08.
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Song, X., Zhou, H. & Liu, G. Human behavior recognition based on multi-feature fusion of image. Cluster Comput 22 (Suppl 4), 9113–9121 (2019). https://doi.org/10.1007/s10586-018-2073-7
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DOI: https://doi.org/10.1007/s10586-018-2073-7