Video surveillance system has been widely used to monitor activity across large application environments such as: transport systems, banks, shopping malls, car parks, and hospitals.
Security has become a critical problem in recent decades and there is a specific need to enable video surveillance system in most of the public places.
A framework for road change detection and map updating
In this field, the learning of the behaviour of traffic is observed to be the most complicated task, particularly in largely changing environments . The other complexity in traffic video surveillance system is that, it must handle large amount of data in real time.
Thus, handling large data in video surveillance system has become an active research area in recent years in distributing computing environment .
The proposed work also focuses on most important field in traffic video surveillance applications namely moving object detection and classification .
When surveillance cameras are taking video file as an input into the surveillance systems, background subtraction method  is applied for segmenting the background and foreground objects for efficient object detection and classification process.
Then, the distributed video files are enhanced with Gaussian filtering.
Efficient object classification and detection is carried out using Linear Discriminant Analysis (LDA) with Support Vector Machine (SVM) for traffic monitoring using video files from surveillance camera.
Human motion analysis relates the detection, tracking and recognition of activities of the people, and more generally, the understanding of human behaviours, from image sequences involving humans.
The road side traffic video surveillance aims at using several image processing methods to obtain better traffic and road safety, which in turn provides direct solution for to reducing death rate of accident victims.
Thus, initially, the background subtraction processes applied into map reduce frame work  in distribute file system environment, to reduce the scalability problem and also an efficient noise removal process is carried out to enhance the video files from the distributing frame work for further process. Section 2 provides a related work based on the proposed technique, in Section 3 the proposed methodology of video surveillance system in traffic scenes is discussed in detail. Finally, Section 5 makes a conclusion about this proposed method. proposed a real-time object detection algorithm for night-time visual surveillance in .
This algorithm is based on contrast analysis wherein the contrast in local change over time is used to detect potential moving objects.
Modern video surveillance systems integrate image analysis techniques for efficient image transmission, event-based attention, and modelbased understanding of sequence.