Research On Scene Analysis Technology In Intelligent Video Surveillance System

Scene analysis technology in intelligent video surveillance system is to extract feature from scene information according to the feature of the scene information to analysis and classify. The objects have same properties classified as a class and analysis the scene contains what objects. Play to the initiative of machine vision, the machine can automatically analyze and extract scene information to achieve the purpose of scene analysis when nobody monitoring. Scene analysis technology in intelligent video surveillance system is a challenging cross-subject, involving research field of biometrics, pattern recognition, image processing, machine vision and others. It is an important component of affective computing and human-computer interaction. It is also a research hotspot in the field of pattern recognition and artificial intelligence. So scene analysis has high academic value and a huge potential in market applications.In recent years, due to the joint efforts of major research institutes, well-known universities and IT companies both at home and abroad, the technology of scene analysis technology in intelligent video surveillance system developed rapidly. This paper is based on previous research in related fields, through referring to and analyzing large amounts of academic papers and literature of scene analysis technology in intelligent video surveillance system at home and abroad, efforts to study the key techniques and methods scene feature extraction and analysis. Purpose of this paper is to seek algorithms with achieving relatively simple, speed of feature extraction and analysis, high recognition rate and is suitable for facial expression recognition. In this paper, as follows:1. From the now technology, this paper comprehensive overview the background and significance, the purpose and applications, research status and development trend of scene analysis technology in intelligent video surveillance system. Then, from the Markov Random Field and scene feature description, sorting the main techniques on Markov Random Field, and use it in scene analysis. Some common feature extraction and classification of scene analyzed.2. This paper proposes an efficient scene analysis and description based on MRF; using GMRF to extracted the texture features in luminance of YCbCr. In LUV color space to extracted brightness and color, combined with the MRF-MAP model and Bayesian theory to analyze scene, Iterative calculation using the ICM algorithm, the parameters by EM algorithm estimated. In order to verify the validity and robustness this algorithm, this paper did two sets of experiments to contrast, experiments results show that this algorithm had a certain ability of scene analysis, and feature extraction speed was faster than other methods.