Human Activity Recognition Based On 2d Texture Signal Pattern Analysis

P. Sathiya 1 *, P. AnandhaKumar 1
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1 Anna University, India
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 6, Issue September 2016 - Special Issue, pp. 53-66. https://doi.org/10.30935/ojcmt/5661
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ABSTRACT

Human activity recognition is an important research area of computer vision which dictates the need to automatically detect and retrieve semantic events in videos based on video contents. In this paper, we attempt to extract the foreground object from the video clip using color model and generate a unique signal pattern for the detected foreground (human). Signal pattern is generated for the extracted 2D texture features and the most significant features are selected using feature selection method. For each detected object, we can study its corresponding motion pattern, entry/exist points, and behavior patterns. Based on this information, it is efficient to improve the object detection and track the abnormal event occurrence. Experiments were performed on KTH dataset, High-Level Human interaction dataset and real time video dataset. The empirical results show that 85% of accuracy based on precision/recall measure was obtained, and the ability to recognize the activities in real time shows the promise for applied use.

CITATION

Sathiya, P., & AnandhaKumar, P. (2016). Human Activity Recognition Based On 2d Texture Signal Pattern Analysis. Online Journal of Communication and Media Technologies, 6(September 2016 - Special Issue), 53-66. https://doi.org/10.30935/ojcmt/5661

REFERENCES

  • Ding, M., & Fan, G. (2016). Articulated and Generalized Gaussian Kernel Correlation for Human Pose Estimation. IEEE Transactions on Image Processing, 25(2), 776-789.
  • Dollár, P., Rabaud, V., Cottrell, G., & Belongie, S. (2005, October). Behavior recognition via sparse spatio-temporal features. In 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (pp. 65-72). IEEE.
  • Gaglio, S., Re, G. L., & Morana, M. (2015). Human activity recognition process using 3-D posture data. IEEE Transactions on Human-Machine Systems, 45(5), 586-597.
  • Iosifidis, A., Tefas, A., & Pitas, I. (2012). Activity-based person identification using fuzzy representation and discriminant learning. IEEE Transactions on Information Forensics and Security, 7(2), 530-542.
  • Ke, Y., Sukthankar, R., & Hebert, M. (2005, October). Efficient visual event detection using volumetric features. In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 (Vol. 1, pp. 166-173). IEEE.
  • Laptev, I. (2005). On space-time interest points. International Journal of Computer Vision, 64(2-3), 107-123.
  • Leibe, B., Leonardis, A., & Schiele, B. (2008). Robust object detection with interleaved categorization and segmentation. International journal of computer vision, 77(1-3), 259-289.
  • Lin, W., Chen, Y., Wu, J., Wang, H., Sheng, B., & Li, H. (2014). A new network-based algorithm for human activity recognition in videos. IEEE Transactions on Circuits and Systems for Video Technology, 24(5), 826-841.
  • Mukherjee, S., Biswas, S. K., & Mukherjee, D. P. (2011). Recognizing human action at a distance in video by key poses. IEEE Transactions on Circuits and Systems for Video Technology, 21(9), 1228-1241.
  • Oikonomopoulos, A., Pantic, M., & Patras, I. (2009). Sparse B-spline polynomial descriptors for human activity recognition. Image and vision computing, 27(12), 1814-1825.
  • Schuldt, C., Laptev, I., & Caputo, B. (2004, August). Recognizing human actions: a local SVM approach. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Vol. 3, pp. 32-36). IEEE.
  • Singh, M., Basu, A., & Mandal, M. K. (2008). Human activity recognition based on silhouette directionality. IEEE Transactions on Circuits and Systems for Video Technology, 18(9), 1280-1292.
  • Wiliem, A., Madasu, V., Boles, W., & Yarlagadda, P. (2012, April). A Context Space Model for Detecting Anomalous Behaviour in Video Surveillance. In Information Technology: New Generations (ITNG), 2012 Ninth International Conference on (pp. 18-24). IEEE.