Vehicle Detection and Tracking Based on Interest Points of Visual Appearance

This paper presents a unique pattern of vehicle detection with the help of fundamental and successive algorithms. The characteristics of the vehicle are the important parameters to identify vehicles. A good number of corner points is compacted inside a vehicle region which is considered as the initial requirement for an algorithm. The densely packed corner points are grouped. This grouping gives a hint of points which are associated with each vehicle and they play a key in detection of vehicles. Once the grouping is performed, the non-vehicle region is segmented. The corner points are tracked with a Lucas-Kanade algorithm in order to maintain the stability of corner points. The detection rate with the proposed method is 93.95%.