Research On Pedestrian Detection Methods Based On Appearance Characteristics And Software Implementation

As an important field of computer vision problems, pedestrian detection have been extensively studied. Pedestrian detection has a wide range of applications, for its role as pre-processing for action recognition, behavior analysis and pedestrian tracking task. The typical applications of pedestrian detection include: driver assistance and autonomous vehicles, human-computer interaction, visual monitoring and behavior analysis, and image analysis and retrieval based on pedestrian content. Pedestrian detection is a challenging task due to the complex changes of pedestrian objects in images and videos caused by factors such as joints of the body, illumination, occlusion, background interference and shooting conditions. Meanwhile, from a viewpoint of users, the pedestrian detection system should satisfy the real-time processing demand.Based on the large numbers of pedestrian detection algorithms, we chose three representative and far-reaching detection algorithms to make a detailed analysis and deep research.1. Motion and appearance pattern based pedestrians detection. In this thesis, we introduced and analyzed a kind of pedestrian detection methods using motion pattern and appearance information. The method achieves feature extraction related to motion and appearance by operation on successive video frames, and then use the Ada Boost cascade classifier framework for pedestrian detection. The method is provided with high detection speed with high detection accuracy.2. Pedestrian detection using the histogram of oriented gradient(HOG). HOG has become a mainstream feature for pedestrian detection for its simplicity and powerful ability to describe pedestrian object. The feature of HOG is not only tolerant to the variance of scale changing, stretching to transform the impact of the advantages, but also can reflect the local shape information and is robust to illuminations.3. Pedestrian detection by covariance feature and spatial-temporal information. The covariance feature is firstly proposed by Tuzel, and achieves good results on pedestrian detection in static images. After that, this method is developed for detecting pedestrian from both static images and video sequences. The method achieves better detection performance than the previous methods in detectionaccuracy and detection efficiency.The paper analyzed and investigated details of the three pedestrian detection algorithms above and discussed involved theory. The ability of these methods is deeply evaluated. Through detailed experiments on benchmark datasets, we verified the effects and efficiency of related pedestrian detection algorithms in the context of still images and videos. Based on results of experiments, we obtained some significant conclusions and make a useful guide to apply the pedestrian detection method in practice.