This thesis addresses the two core problems of vision based advanced driver assistance systems, i.e., robustness of the system to the dynamic changes in the lighting conditions and real time processing.
The first problem is related with the continuous variations in the lighting conditions of a dynamic environment. It is well-known that to capture a good image, we need sufficient lighting in the scene which enables the sensor to reproduce a good image out of that real scene.
But, in a real dynamic environment, there is no guarantee that ideal lighting conditions will always sustain. As a result, we may have a sequence of good frames and a sequence of poor frames grabbed by the ADAS. This dependency of ADAS on the dynamic variations in the lighting of the environment leads to an unacceptable performance of the system.
In this thesis, a novel solution is proposed to address this problem which is based on nonlinear oscillatory theory. Settings of the most appropriate parameters of a coupled oscillatory system are derived through stability/bifurcation analysis under which system is highly robust to the dynamic changes in its initial conditions. This property of the system has been successfully tested to handle the dynamic variations in the lighting conditions. The issue of real-time processing has been approached by proposing a novel framework that integrates the proposed oscillatory paradigm with cellular neural network. This integration leads to use any existing analog CNN-UM chip to realize real time processing.