Automatic object detection in car-driving sequence using neural network and optical flow analysis

The increase in the number of vehicles has seen a rise in the accident rate. Pedestrians and vehicles on the move affect the behavior of the driver extensively. As an effort to reduce the number of accidents by assisting the driver, we propose a software system based on neural network and optical flow analysis to detect objects as observed by a driver. The proposed system tracks vehicles by detecting its number plate and the optical flow associated with it. It has four stages. First, the system is trained by using a number of images. In the second stage, object detection is performed for each image of the sequence using the trained system. Following this, the optical flow is calculated for each image as an estimate of the motion in the scene. In the fourth and final stage, optical flow is combined with the detected objects and both their direction and magnitude are identified. The results obtained detects cautions and alerts the driver about the impending dangerous situations that might befall. This software is also suitable for systems to study the behavior of the driver and if the software is used in all vehicles on the road, creating a database of the detected number plates, it can give the location of the stream of vehicles on the road.