This diploma paper focuses on the problem of vehicle detection in images with a development and evaluation of a new vehicle detector. The vehicle detector uses the descriptors of a histogram of oriented gradient (HOG) and the support vector machine (SVM) classifier. To compare its functioning to the functioning of a working detector, the Haar cascade classifier is used and the results are evaluated with the precision-recall curve.
The introduction starts off with a presentation of similar works that detail the history of developing methods for detecting vehicles in image data and a rough comparison between them. This description is followed by a chapter on theoretical bases of some integral parts for detecting an object in an image. The theoretical chapter only focuses on the processes and methods of learning, which are later used in the implementation of the new detector.
In the next part of the diploma paper, the process of extracting descriptors via the histogram of oriented gradient in combination with the support vector machine classifier is described. The other implemented method is the implementation and detection with the help of the Haar cascade method.
The second part focuses on the detector’s developmental process. It includes a description and demonstration of the acquisition of the database, the use of the HOG process for the acquisition of attributes, the learning process of the SVM detector, and the use of the detector. This is followed by a description of the influence of different parameters on successful detection and the effect of pre-treatment on detection. The paper then concludes with a comparison of methods based on the effectiveness and the speed of object recognition.