Abstract
We present an effective algorithm to detect essential body-joints and their corresponding atomic actions from a series of human activity data for efficient human activity recognition/classification. Our human activity data is captured by a RGB-D camera, i.e. Kinect, where human skeletons are detected and provided by the Kinect SDK. Unique in our approach is the novel encoding that can effectively convert skeleton data into a symbolic sequence representation which allows us to detect the essential atomic actions of different human activities through longest common subsequence extraction. Our experimental results show that, through atomic action detection, we can recognize human activity that consists of complicated actions. In addition, since our approach is “simple”, our human activity recognition algorithm can be performed in real-time.
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References
Wang, L., David Suster, D.: Recognizing human activities from silhouettes: Motion subspace and factorial discriminative graphical model. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Weinland, D., Boyer, E.: Action recognition using exemplar-based embedding. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2008)
Wang, Y., Huang, K., Tan, T.: Human activity recognition based on r transform. In: Workshop of IEEE Conference on Computer Vision and Pattern Recognition for Visual Surveillance, pp. 1–8 (2007)
Souvenir, R., Babbs, J.: Learning the viewpoint manifold for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2008)
Wang, L., Suter, D.: Informative shape representations for human action recognition. In: International Conference on Pattern Recognition, pp. 1266–1269 (2006)
Huang, F., Xu, G.: Viewpoint Insensitive Action Recognition Using Envelop Shape. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 477–486. Springer, Heidelberg (2007)
Cherla, S., Kulkarni, K., Kale, A., Ramasubramanian, V.: Towards fast, view-invariant human action recognition. In: Workshop of IEEE Conference on Computer Vision and Pattern Recognition for Human Communicative Behaviour Analysis, pp. 1–8 (2008)
Souvenir, R., Babbs, J.: Learning the viewpoint manifold for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2008)
Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding 104, 249–257 (2006)
Huang, W., Wu, Q.M.J.: Human action recognition based on self organizing map. In: IEEE International Conference on Acoustics Speech and Signal Processing, pp. 2130–2133 (2010)
Ahmad, M., Lee, S.W.: Variable silhouette energy image representations for recognizing human actions. Image and Vision Computing 28, 814–824 (2010)
Abdelkader, M.F., Abd-Almageed, W., Srivastava, A.: Silhouette-based gesture and action recognition via modeling trajectories on riemannian shape manifolds. Computer Vision and Image Understanding 115, 439–455 (2011)
Jia, K., Yeung, D.Y.: Human action recognition using local spatio-temporal discriminant embedding. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)
Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as spatio-temporal shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 2247–2253 (2007)
Corporation Microsoft.: Kinect for xbox 360 (2010)
Shotton, J., Fitzgibbon, A., Cook, M., Blake, A.: Real-time human pose recognition in parts from single depth images. In: IEEE Conference on Computer Vision and Pattern Recognition (2011)
Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28, 976–990 (2010)
Aggarwal, J., Ryoo, M.: Human activity analysis: A review. ACM Computing Surveys 43, 1–43 (2011)
Sung, J., Ponce, C., Selman, B., Saxena, A.: Unstructured human activity detection from rgbd images. In: IEEE International Conference on Robotics and Automation, pp. 842–849 (2012)
Tran, K., Kakadiaris, I., Shah, S.K.: Part-based motion descriptor images for human action recognition. Pattern Recognition 45, 2562–2572 (2012)
Ryoo, M., Aggarwal, J.: Semantic representation and recognition of continued and recursive human activities. International Journal of Computer Vision 82, 1–24 (2009)
Chakraborty, B., Bagdanov, A.D., Gonzalez, J., Roca, X.: Human action recognition using an ensemble of body-part detectors. Expert System (2011)
Hirschberg, D.S.: Algorithms for the longest common subsequence problem. Journal of the ACM 24, 664–675 (1977)
Bergroth, L., Hakonen, H., Raita, T.: A survey of longest common subsequence algorithms. In: 7th International Symposium on String Processing and Information Retrieval, pp. 39–48 (2000)
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Jin, SY., Choi, HJ. (2013). Essential Body-Joint and Atomic Action Detection for Human Activity Recognition Using Longest Common Subsequence Algorithm. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37484-5_13
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DOI: https://doi.org/10.1007/978-3-642-37484-5_13
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