| dc.description.abstract | With the increase of public vehicles, traffic accidents increase also followed the case. Traffic accidents have become a major problem affecting the safety of the public, because the driver′s distraction or negligence collision pedestrians heard. Therefore, we propose a computer using a monocular vision pedestrian detection system in the study, applied to the general campus streets and alleys, using driving assistance systems as a reminder to driver, in order to avoid pedestrian collision accidents.
Our propose pedestrian detection system is divided into three steps: First, the use of pedestrian detection strip (PDS) to calculate the position of the image in the foreground objects, define the candidate object window. Second, each window would template matching for pedestrian silhouette to identify suspected pedestrian objects window to do the follow-up verification, in order to reduce the computational lot of classification. Third, using the histograms of oriented gradients (HOG) describe characteristics of pedestrians, and on the classification decision, the use of support vector machines (SVM) as a classifier for pedestrian samples nine regional individual learning and training. Finally, the combinations of these classifiers using AdaBoost were learning the weights, as a system based on the final identification of pedestrians. Training of regional learning classifiers can reduce the problem because the line of sight angle and pedestrians partially obscured can determine for each individual contour pedestrians to integrate through AdaBoost classifier results, making the overall classification ability has improved.
In the experimental analysis, we retrieve the actual streets and general campus traffic movie, which contains images with a variety of daytime sunshine circumstances. At step in object detection, object position by capturing of PDS can be achieved with detection rate of 99.6%. Template matching can filter out more than 70% of non-pedestrian window, and accelerate the subsequent AdaBoost-SVM confirmation process. Using the same HOG features, AdaBoost with multiple SVM proposed in this study is about 87% detection rate and false positive rate of 4% is detected in a variety of environments, and compare single SVM detection rate of only 83% to detect rate and false positive rate of 7%, can be explained classification framework used in this study can be compared with the response to environmental factors that affect the shape changing pedestrians. Finally, the system in general can perform computing approximately 20 images per second.
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