Fuzzy Logic-Based Scenario Recognition from Video Sequences

Abstract

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 inscenario recognition: Low level processing, including moving object detection and object tracking, and featureextraction. We have developed new features through this work which are RUD (relative upper density), RMD (relativemiddle 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 eachframe and scenario recognition for sequence of images. This part is the focus of our research, and different patternrecognition and classification methods are implemented and experimental results are analyzed. We looked intoseveral methods of classification which are decision tree, frequency domain classification, neural network-basedclassification, 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.