Pedestrian Detection Algorithm Based On Multi-scale Orientation Feature

With the development of human society , there are more and more social insecurity factors at the same time. Each terrorist attack on the international gives a wake-up call for every national security departments. As a result, many countries pay more and more attentions to the use of video surveillance technology to the important sectors, sensitive locations, such as to monitor public places. Although the object detection technology such as face detection and license plate detection have become more mature, however the high reliability detection of moving human which is on the complex environment is still faced with great difficulties. Meanwhile, to carry out the research also has important theoretical significance to the objective model expression as well as the core of this area.At the present, the description of the main features contains tow parts which are the processing of color and the extraction of the outline. The most representative descriptors are Viola's Haar-Like and Dalai's HOG., and achieve good results in human detection. In this thesis, we put forward a new descriptor-Multi-scale orientation feature. The new features not only include the two characteristics of their respective advantages, but also make up for their lack. This feature is described according to the shape of the characteristics region, and the set of all the features describe the different orientation of each scale in a picture. Training these features in SVM and AdaBoost separately and use the training result to detect the human in video and picture. According to the test on the public testing sets and our own testing sets in this thesis, with the analysis of the result comparing to other international algorithms, it can proves that: on the same condition of detection principles, our algorithms have shown a clear advantage both on the computing speed and the accuracy of detection result.