| Summary: | 碩士 === 國立臺灣科技大學 === 機械工程系 === 103 === The purpose of this thesis is using a RGB camera to implement recognition and tracking of pedestrian. In this thesis, Histogram of Oriented Gradient(HOG) is used to extract the features of pedestrian, these features to are used to train a SVM as our pedestrian detector to detect pedestrians existing in the image acquired by camera.
To achieve higher detection accuracy rate, we categorize easily misclassified images as Hard Examples, later retrain the detector to increase detection rate and reduce false-positive rate.
After the pedestrian has been detected, Tracking Learning Detection algorithm is applied to track target pedestrian, also Supervised Bootstrapping method is used to train a classifier with labeled samples and update the classifier with new samples acquired during tracking procedure, such that the classifier adapts better to the variation of target position and surrounding. Through forward-backward error method feature points with better performance and be selected during tracking procedure, with the tracking results and the classifier under each other’s supervision, combing both results as pedestrian tracking result. Estimating target’s actually 3-D position through acquired pedestrian 2-D coordinate gives the proposed system more flexibility with other applications.
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