SciELO - Scientific Electronic Library Online

vol.11 número5Fiber Optic Pressure Sensor of 0-0.36 psi by Multimode Interference TechniqueWeb Tools 2.0 for Health Promotion in Mexico índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados




Links relacionados

  • No hay artículos similaresSimilares en SciELO


Journal of applied research and technology

versión On-line ISSN 2448-6736versión impresa ISSN 1665-6423

J. appl. res. technol vol.11 no.5 México oct. 2013


Fuzzy Logic-Based Scenario Recognition from Video Sequences


E. Elbaşi


The Scientific and Technological Research Council of Turkey, Tunus Caddesi, No: 80 Kavaklidere, Ankara, Turkey



In recent years, video surveillance and monitoring have gained importance because of security and safety concerns. Banks, borders, airports, stores, and parking areas are the important application areas. There are two main parts in scenario recognition: Low level processing, including moving object detection and object tracking, and feature extraction. We have developed new features through this work which are RUD (relative upper density), RMD (relative middle density) and RLD (relative lower density), and we have used other features such as aspect ratio, width, height, and color of the object. High level processing, including event start-end point detection, activity detection for each frame and scenario recognition for sequence of images. This part is the focus of our research, and different pattern recognition and classification methods are implemented and experimental results are analyzed. We looked into several methods of classification which are decision tree, frequency domain classification, neural network-based classification, Bayes classifier, and pattern recognition methods, which are control charts, and hidden Markov models. The control chart approach, which is a decision methodology, gives more promising results than other methodologies. Overlapping between events is one of the problems, hence we applied fuzzy logic technique to solve this problem. After using this method the total accuracy increased from 95.6 to 97.2.

Keywords: Scenario recognition, high level processing, control chart, fuzzy logic.





[1] E. Elbasi et al., "Control Charts Approach for Scenario Recognition in Image Sequences", Turkish Journal of Electrical Eng. & Computer Sciences Volume 13, Issue 3, 2005.         [ Links ]

[2] S. Hongeng et al., "Video-based event recognition: activity representation and probabilistic recognition methods", Computer Vision And Image Understanding Volume: 96 Issue: 2 Pages: 129-162, 2004.         [ Links ]

[3] M.Hamid, "ARGMode-Activity recognition using graphical models", CVPR, 2003.         [ Links ]

[4] J.Yamato et al., "Human action recognition using HMM with categoriy separated vector quantiztion", IEIC, 1994.         [ Links ]

[5] A. Veeraraghavan et al.,"Rate-Invariant Recognition of Humans and Their Activities", IEEE Transactions On Image Processing,Volume: 18 Issue: 6 Pages: 1326-1339, 2009.         [ Links ]

[6] A. Amer, "A computational framework for simultaneous real-time high level video representing", Multisensor Surveillance Systems, pp 149-182, 2003.         [ Links ]

[7] J.W.Davis and A.Tyagi, "A reliable inference framework for recognition of human actions", Advanced Video and Signal Based Surveillance, 2003.         [ Links ]

[8] YQ Ma et al., "Motion Segmentation and Activity Representation in Crowds", 12th International Workshop on Combinatorial Image Analysis, April 07-09, 2008.         [ Links ]

[9] T. Jaeggli et al., "Learning Generative Models for Multi-Activity Body Pose Estimation", International Journal Of Computer Vision Volume: 83 Issue: 2 Pages: 121-134, 2009.         [ Links ]

[10] P. Turaga et al., "Unsupervised view and rate invariant clustering of video sequences", IEEE Conference on Computer Vision and Pattern Recognition, 2007.         [ Links ]

[11] D. Anderson et al., "Modeling Human Activity From Voxel Person Using Fuzzy Logic", IEEE Transactions On Fuzzy Systems Volume: 17 Issue: 1 Pages: 39-49, 2009.         [ Links ]

[12] ZP Zhao and A. Elgammal, "Human Activity Recognition from Frame's Spatiotemporal Representation", 19th International Conference on Pattern Recognition (ICPR 2008), 2008.         [ Links ]

[13] D. Gehrig and T. Schultz, "Selecting Relevant Features for Human Motion Recognition", 19th International Conference on Pattern Recognition (ICPR 2008), 2008.         [ Links ]

[14] L. Gao et al., "Automatic Learning of Semantic Region Models for Event Recognition", 8th International Conference on Intelligent Systems Design and Applications (ISDA), 2008.         [ Links ]

[15] MAR Ahad et al., "Human Activity Recognition: Various Paradigms", International Conference on Control, Automation and Systems, 2008.         [ Links ]

[16] ZX Zhang et al., "Robust Automated Ground Plane Rectification Based On Moving Vehicles For Traffic Scene Surveillance", 15th IEEE International Conference on Image Processing (ICIP 2008), 2008.         [ Links ]

[17] N. Vázquez et al., "Automatic System for Localization and recognition of vehicle plate numbers", Journal of Applied Research and Technology, Volume 1, Number 1, page 63-77, 2003.         [ Links ]

[18] L. Ramirez-Valdez and R. Hasimoo-Beltran, "3D Facial Expression Synthesis and its Application to Face Recognition Systems", Journal of Applied Research and Technology, Volume 7, Number 3, page 323-339,2009.         [ Links ]

[19] E. Arslan et al., "Classification of Fibromyalgia Syndrome by using fuzzy logic method", BIYOMUD 2010.         [ Links ]

Creative Commons License Todo el contenido de esta revista, excepto dónde está identificado, está bajo una Licencia Creative Commons