Abstract
In this paper we present a human action recognition system that utilizes the fusion of depth and inertial sensor measurements. Robust depth and inertial signal features, that are subject-invariant, are used to train independent Neural Networks, and later decision level fusion is employed using a probabilistic framework in the form of Logarithmic Opinion Pool. The system is evaluated using UTD-Multimodal Human Action Dataset, and we achieve 95% accuracy in 8-fold cross-validation, which is not only higher than using each sensor separately, but is also better than the best accuracy obtained on the mentioned dataset by 3.5%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, C., Jafari, R., Kehtarnavaz, N.: A survey of depth and inertial sensor fusion for human action recognition. Multimed. Tools Appl. 76(3), 4405–4425 (2017)
Stein, S., McKenna, S.J.: Combining embedded accelerometers with computer vision for recognizing food preparation activities. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM (2013)
Ming, Y., Wang, G., Fan, C.: Uniform local binary pattern based texture-edge feature for 3D human behavior recognition. PloS one 10(5), e0124640 (2015)
Ustundag, B.C., Unel, M.: Human action recognition using histograms of oriented optical flows from depth. In: Bebis, G., et al. (eds.) ISVC 2014. LNCS, vol. 8887, pp. 629–638. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-14249-4_60
Han, J., Shao, L., Xu, D., Shotton, J.: Enhanced computer vision with microsoft kinect sensor: a review. IEEE Trans. Cybernet. 43(5), 1318–1334 (2013)
Aggarwal, J.K., Xia, L.: Human activity recognition from 3d data: a review. Pattern Recogn. Lett. 48, 70–80 (2014)
Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1110–1118 (2015)
Nie, B.X., Xiong, C., Zhu, S.C.: Joint action recognition and pose estimation from video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1293–1301 (2015)
Altun, K., Barshan, B., Tunçel, O.: Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recogn. 43(10), 3605–3620 (2010)
Qaisar, S., et al.: A hidden markov model for detection & classification of arm action in cricket using wearable sensors. J. Mob. Multimed. 9(1&2), 128–144 (2013)
Ermes, M., Parkka, J., Mantyjarvi, J., Korhonen, I.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans. Inf. Technol. Biomed. 12(1), 20–26 (2008)
Ofli, F., et al.: Berkeley MHAD: a comprehensive multimodal human action database. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp. 53–60. IEEE (2013)
Chen, C., Jafari, R., Kehtarnavaz, N.: A real-time human action recognition system using depth and inertial sensor fusion. IEEE Sens. J. 16(3), 773–781 (2016)
Chen, C., Jafari, R., Kehtarnavaz, N.: UTD-MHAD: a multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In: Proceedings of IEEE International Conference on Image Processing (2015)
Li, W., Chen, C., Su, H., Du, Q.: Local binary patterns for spatial-spectral classification of hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 53(7), 3681–3693 (2015)
Orfanidis, S.J.: Introduction to Signal Processing. Prentice-Hall, Englewood Cliffs (1996)
Scales, L.E.: Introduction to Non-Linear Optimization. Springer, New York (1985)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Fuad, Z., Unel, M. (2018). Human Action Recognition Using Fusion of Depth and Inertial Sensors. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-93000-8_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92999-6
Online ISBN: 978-3-319-93000-8
eBook Packages: Computer ScienceComputer Science (R0)