Eye tracking offers a practical solution for monitoring cognitive performance in real world tasks. However, eye tracking in dynamic environments is difficult due to high spatial and temporal variation of stimuli, needing further and thorough investigation. In this paper, we study the possibility of developing a novel computer vision assisted eye tracking analysis by using fixations. Eye movement data is obtained from a long duration naturalistic driving experiment. Source invariant feature transform (SIFT) algorithm was implemented using VLFeat toolbox to identify multiple areas of interest (AOIs). A new measure called 'fixation score' was defined to understand the dynamics of fixation position between the target AOI and the non target AOIs. Fixation score is maximum when the subjects focus on the target AOI and diminishes when they gaze at the non-target AOIs. Statistically significant negative correlation was found between fixation score and reaction time data (r =-0.2253 and p<0.05). This implies that with vigilance decrement, the fixation score decreases due to visual attention shifting away from the target objects resulting in an increase in the reaction time.