Image processing and vision techniques for smart vehicles

The idea of safe and smart vehicles has been thoroughly researched over the past decades to ensure drivers´ safety from possibly dangerous situations. This paper presents a brief review of different applications of image processing and computer vision techniques in smart vehicles. To detect other on-road vehicles, researchers have approached the problem from various angles; with solutions ranging from active sensors like radar to passive sensors like cameras. Recently, researchers are working to create a panoramic 360 degree view of the vehicle´s environment by merging different images from sides, rear and front of the car using passive sensors. There has also been work on constructing high resolution images from low cost, low resolution cameras, in order to reduce final cost of the system. In this paper, we have presented a new algorithm for mono-camera based vehicle detection systems, by incorporating different low level (edges) and high level features (Bag-of-features). To extract edge information flawlessly, we presented a new edge detection method, namely Difference of BiGaussian (DoBG). Experimental results show average 98.5% recognition rates, which is one of the best results achieved so far.