| Summary: | 碩士 === 國立中央大學 === 資訊工程研究所 === 98 === Developing a practical driver assistance system for ensuring driving safety has become an increasingly important issue. The major risk of driving on the highway comes from possible collisions of the vehicle with the preceding one because a suitable distance is not well maintained. Therefore, knowing the relative position of the preceding vehicle and the surrounding cars should significantly reduce the risks. In this thesis, we would like to develop a highway preceding vehicle detection/tracking scheme, in which a monocular vision-based system for detecting the preceding vehicle in close and mid-range view will be designed to help provide a better view for the drivers.
Our approach is based on an appearance-based methodology, i.e. Implicit Shape Model. A codebook is built for vehicle detection and tracking by using the training images captured from the real scenes. The collection of training images are divided into three parts: fully rear view, partially rear view from left and from the right sides. By applying scale-invariant feature transform (SIFT) to extract the interest points, we have a set of good features presenting the preceding vehicles. Then, we group those features to build up the codebook by clustering. Three models will thus be constructed. For detection and tracking the objects, we apply SIFT detector again in the real scenes. In each scene, we compare the extracted features with the codebook to find its matched representative features. Once a model is found, we can identify the ROI based on the scale and position indicated in the models. We can continue searching for the left and right side of pre-identified ROI to detect more possible vehicles. The experimental results show that vehicles can be detected in each of the three areas, i.e. right in front of the diver and his left/right-hand side areas.
|
|---|
