Considering todays growing society and developing technologies which are co-influential

between each other, there is a larger scope of security concerns, traffic congestion due to

improper planning and hence a greater need of more intelligent video surveillance.

In this thesis, we have worked on developing such intelligent video surveillance

system which mainly focusses on cell area such as parking spaces. The system operates on

outdoor environment with a stationary camera; the main objective of this system is detecting and tracking of moving objects mainly cars.

Two detection algorithms were developed using optical flow as core strategy. In the

first algorithm the flow vectors were classified based on their magnitude and orientation; the

GOMAG algorithm. The second algorithm used K-means method on the flow vectors to

achieve the classification for moving object detection; the SKMO algorithm.

A comparison analysis was done between the proposed algorithms and well known

detection algorithms of background modeling and Otsu’s segmentation of flow vectors. The

both proposed algorithms performed significantly better than background modeling and

Otsu’s segmentation of flow vectors algorithms. The SKMO algorithm showed better

stability and processed time efficiency than the GOMAG algorithm.