Abstract
In this paper, we present a simple new human activity recognition method using discrete cosine transform (DCT). The scheme uses the DCT coefficients extracted from silhouettes as descriptors (features) and performs frame-by-frame recognition, which make it simple and suitable for real time applications. We carried out several tests using radial basis neural network (RBF) for classification, a comparative study against stat-of-the-art methods shows that our technique is faster, simple and gives higher accuracy performance comparing to discrete transform based techniques and other methods proposed in literature.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ke, S.-R., Thuc, L.H.L., Lee, Y.-J., Hwang, J.-N., Yoo, J.-H., Choi, K.-H.: A review on video-based human activity recognition. Computers 2(2), 88–131 (2013)
Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: Proceedings of ICCV, pp. 1395–1402 (2005)
Dollár, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: Proceedings of the 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking And Surveillance, Beijing, China, pp. 65–72, 15–16 October 2005
Kumari, S., Mitra, S.K.: Human action recognition using DFT. In: Proceedings of the Third IEEE National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Hubli, India, pp. 239–242, 15–17 December 2011
Ahmad, T., Rafique, J.: Using discrete cosine transform based features for human action recognition. J. Image Graph. 3(2) (2015)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, vol. 2, pp. 1150–1157, 20–25 September 1999
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA, vol. 1, pp. 886–893, 20–26 June 2005
Lu, W., Little, J.J.: Simultaneous tracking and action recognition using the PCA-HOG descriptor. In: Proceedings of the 3rd Canadian Conference on Computer and Robot Vision, Quebec, PQ, Canada, p. 6, 7–9 June 2006
Ojala, T., Pjetikainen, M.: Multiresolution grey-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)
Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. PAMI 29(6), 915–928 (2007)
Lin, C., Hsu, F., Lin, W.: Recognizing human actions using NWFE-based histogram vectors. EURASIP J. Adv. Sig. Process. 2010, 9 (2010)
Nakazawa, A., Kato, H., Inokuchi, S.: Human tracking using distributed vision systems. In: Proceedings of IEEE Fourteenth International Conference on Pattern Recognition, Brisbane, QLD, Australia, vol. 1, pp. 593–596, 20 August 1998
Huo, F., Hendriks, E., Paclik, P., Oomes, A.H.J.: Markerless human motion capture and pose recognition. In: Proceedings of the 10th IEEE Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), London, UK, pp. 13–16, 6–8 May 2009
Sedai, S., Bennamoun, M., Huynh, D.: Context-based appearance descriptor for 3D human pose estimation from monocular images. In: Proceedings of IEEE Digital Image Computing: Techniques and Applications (DICTA), Melbourne, VIC, Australia, pp. 484–491, 1–3 December 2009
Abdrhman, A., Ukasha, M.: Contour compression of image watermarking using DCT transform & ramer method. In: 2nd International Conference on Mechanical, Electronics and Mechatronics Engineering (ICMEME 2013), London, UK, pp, 147–151, 17–18 June 2013
Zhao, R.M., Lian, H., Pang, H.W., Hu, B.N.: A watermarking algorithm by modifying AC coefficies in DCT domain. In: International Symposium on Information Science and Engineering, pp. 159–162. IEEE (2008)
Imtiaz, H., et al.: Human action recognition based on spectral domain features. In: 19th Annual Conference KES-2015, Singapore
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Weizmann database (2007). http://www.wisdom.weizmann.ac.il/~vision/SpaceTimeActions.html
Boiman, O., Irani, M.: Similarity by composition. In: Proceedings of Neural Information Processing Systems (NIPS)
Scovanner, P., Ali, S., Shah, M.: A 3-dimensional SIFT descriptor and its application to action recognition. In: Proceedings of ACM Multimedia, pp. 357–360 (2007)
Wang, L., Suter, D.: Recognizing human activities from silhouettes: motion subspace and factorial discriminative graphical model. In: Proceedings of CVPR, p. 8 (2007)
Kellokumpu, V., Zhao, G., Pietikäinen, M.: Texture based description of movements for activity analysis. In: Proceedings of VISAPP, vol. 1, pp. 206–213 (2008)
Kellokumpu, V., Zhao, G., Pietikäinen, M.: Human activity recognition using a dynamic texture based method. In: Proceedings of BMVC, p. 10 (2008)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th IEEE International Conference on Pattern Recognition (ICPR), Cambridge, UK, vol. 3, pp. 32–36, 23–26 August 2004
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, USA, pp. 1–8, 23–28 June 2008
Foroughi, H., Naseri, A., Saberi, A., Yazdi, H.S.: An eigenspace-based approach for human fall detection using integrated time motion image and neural network. In: Proceedings of IEEE 9th International Conference on Signal Processing, pp. 1499–1503 (2008)
Fiaz, M.K., Ijaz, B.: Vision based human activity tracking using artificial neural networks. In: Proceedings of IEEE International Conference on Intelligent and Advanced Systems (ICIAS), Kuala Lumpur, Malaysia, pp. 1–5, 15–17 June 2010
Duong, T.V., Bui, H.H., Phung, D.Q., Venkatesh, S.: Activity recognition and abnormality detection with the switching hidden semi-markov model. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA, vol. 1, pp. 838–845, 20–25 June 2005
Yamato, J., Ohya, J., Ishii, K.: Recognizing human action in time-sequential images using Hidden Markov Model. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Champaign, IL, USA, pp. 379–385, 15–18 June 1992
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Khelalef, A., Ababsa, F., Benoudjit, N. (2016). A Simple Human Activity Recognition Technique Using DCT. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-48680-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48679-6
Online ISBN: 978-3-319-48680-2
eBook Packages: Computer ScienceComputer Science (R0)