Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Spatio-Temporal Analysis of Trajectory for Pedestrian Activity Recognition

  • Received : 2017.03.21
  • Accepted : 2017.10.24
  • Published : 2018.03.01
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Abstract

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

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References

  1. Kim, Jinpyung, et al. "Crowd Activity Recognition using Optical Flow Orientation Distribution," KSII Transactions on Internet and Information Systems, vol. 9, no. 8, pp. 2948-2963, 2015. https://doi.org/10.3837/tiis.2015.08.011
  2. A. Mehrabian and M. Wiener, "Decoding of inconsistent communications," J. Personality Soc. Psychol., vol. 6, no. 1, pp. 109-114, 1967. https://doi.org/10.1037/h0024532
  3. Turaga, Pavan, et al. "Machine recognition of human activities: A survey," IEEE Trans. Circuits and Systems for Video Technology, vol. 18, no. 11, pp. 1473-1488, 2008. https://doi.org/10.1109/TCSVT.2008.2005594
  4. Hu, Weiming, et al. "Learning activity patterns using fuzzy self-organizing neural network," IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 34, no. 3, pp. 1618-1626, 2004. https://doi.org/10.1109/TSMCB.2004.826829
  5. Naftel, Andrew, and Shehzad Khalid. "Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space," Multimedia Systems, vol. 12, no. 3, pp. 227-238, 2006. https://doi.org/10.1007/s00530-006-0058-5
  6. Li, Ce, et al. "Visual abnormal behavior detection based on trajectory sparse reconstruction analysis," Neurocomputing, vol. 119, pp. 94-100, 2013. https://doi.org/10.1016/j.neucom.2012.03.040
  7. Aggarwal, Jake K., and Michael S. Ryoo. "Human activity analysis: A review," ACM Computing Surveys, vol. 43, no. 3, p. 16, 2011.
  8. Boltes, Maik, et. al. "Automatic extraction of pedestrian trajectories from video recordings," Pedestrian and Evacuation Dynamics 2008. Springer Berlin Heidelberg, pp. 43-54, 2010.
  9. Morris, Brendan Tran, and Mohan Manubhai Trivedi. "A survey of vision-based trajectory learning and analysis for surveillance," IEEE Trans. Circuits and Systems for Video Technology, vol. 18, no. 8, pp. 1114-1127, 2008. https://doi.org/10.1109/TCSVT.2008.927109
  10. Poppe, Ronald. "A survey on vision-based human action recognition," Image and Vision Computing, vol. 28, no. 6, pp. 976-990, 2010. https://doi.org/10.1016/j.imavis.2009.11.014
  11. Chuang, Keh-Shih, et al. "Fuzzy c-means clustering with spatial information for image segmentation," Computerized Medical Imaging and Graphics, vol. 30, no. 1, pp. 9-15, 2006. https://doi.org/10.1016/j.compmedimag.2005.10.001
  12. Homepages.inf.ed.ac.uk, CAVIAR Test Case Scenarios. http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/
  13. Lee, Jae-Gil, et al. "TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering," in Proceedings of the VLDB Endowment, vol. 1, no. 1, pp. 1081-1094, 2008.
  14. Lee, Seon-Woo, and Kenji Mase. "Recognition of walking behaviors for pedestrian navigation," in Proceedings of the IEEE International Conference on Control Applications, pp. 1152-1155, 2001.
  15. Pelekis, Nikos, et al. "Clustering trajectories of moving objects in an uncertain world," Data Mining, 2009. ICDM'09. Ninth IEEE International Conference on. IEEE, pp. 417-427, 2009.
  16. Eom, Ki-Yeol, Jae-Young Jung, and Moon-Hyun Kim. "A heuristic search-based motion correspondence algorithm using fuzzy clustering," International Journal of Control, Automation and Systems, vol. 10, no. 3, pp. 594-602, 2012. https://doi.org/10.1007/s12555-012-0317-5
  17. Dombi, Jozsef. "A general class of fuzzy operators, the DeMorgan class of fuzzy operators and fuzziness measures induced by fuzzy operators," Fuzzy Sets and Systems, vol. 8, no. 2, pp. 149-163, 1982. https://doi.org/10.1016/0165-0114(82)90005-7
  18. Kim, Dae-Won, Kwang H. Lee, and Doheon Lee. "Fuzzy clustering of categorical data using fuzzy centroids," Pattern Recognition Letters, vol. 25, no. 11, pp. 1263-1271, 2004. https://doi.org/10.1016/j.patrec.2004.04.004
  19. Friedl, Mark A., and Carla E. Brodley. "Decision tree classification of land cover from remotely sensed data," Remote Sensing of Environment, vol. 61, no. 3, pp. 399-409, 1997. https://doi.org/10.1016/S0034-4257(97)00049-7
  20. Quinlan, J. Ross. "Induction of decision trees," Machine Learning, vol. 1, no. 1, pp. 81-106, 1986. https://doi.org/10.1007/BF00116251
  21. Cheng, Jie, et al. "Improved decision trees: a generalized version of id3," Machine Learning Proceedings, pp. 100-106, 1988.
  22. Michalski, Ryszard S., Jaime G. Carbonell, and Tom M. Mitchell, eds. "Machine Learning: An Artificial Intelligence Approach," Springer Science & Business Media, 2013.
  23. Stein, Gary, et al. "Decision tree classifier for network intrusion detection with GA-based feature selection," in Proceedings of the 43rd annual Southeast Regional Conference on. ACM, vol. 2, pp. 136-141, 2005.
  24. J. R. Quinlan, "C4.5: Programs for Machine Learning," programs for machine learning. Elsevier, 2014.
  25. Quinlan, J. Ross. "Improved use of continuous attributes in C4. 5," Journal of Artificial Intelligence Research, vol. 4, pp. 77-90, 1996.
  26. Google Code Archive, "Figue - A Collection of Clustering Algorithms Implemented in Javascript," https://code.google.com/archive/p/figue/wikis/Introduction.wiki
  27. Cs.waikato.ac.nz, "Weka 3 - Data Mining with Open Source Machine Learning Software in Java," http://www.cs.waikato.ac.nz/ml/weka/
  28. Lin, Weiyao, et al. "Group event detection with a varying number of group members for video surveillance," IEEE Trans. Circuits and Systems for Video Technology, vol. 20, no. 8, pp. 1057-1067, 2010. https://doi.org/10.1109/TCSVT.2010.2057013