| dc.description.abstract | With the advancement of computer technology and increasing needs of social security, studies of target object detection in aerial surveillance using image processing techniques are growing more and more important. These technologies can be employed in various applications, such as gathering enemy information for military purpose and searching for missing people in mountain areas. In intelligence transportation applications, aerial surveillance not only provides traffic monitoring but also assists traffic management, which can save a lot amount of human resources. Among the basic modules in intelligent aerial surveillance, vehicle detection plays a very important role.
In this thesis, we present an automatic vehicle detection system for aerial surveillance. First of all, background colors are eliminated and then features are extracted. In this system, we consider features including vehicle colors, edges and local feature points. For vehicle color extraction, we utilize color transform to separate vehicle colors and non-vehicle colors effectively. For edge detection, we apply moment-preserving method to adjust the thresholds for canny edge detector automatically, which increases the adaptability and accuracy for detection in various aerial images. Afterwards, a Dynamic Bayesian Network (DBN) is constructed for classification purpose. Based on the features extracted, a well trained DBN can estimate the probability of a pixel belonging to a vehicle or not.
In this work, features are obtained from a neighborhood region but the detection task is based on pixel-wise classification, which is more effective and efficient than multi-scale sliding window or region-based methods. Experiments were conducted on a variety of aerial videos and the average hit rate of testing videos is 92.04%. The number of false positives per frame of testing videos is 0.16616. The experimental results reveal that the proposed approach is feasible for generalization and effective for vehicle detection in various aerial videos.
| en_US |