Department of Electrical EngineeringCollege of Electrical Engineering and Computer ScienceNational Taiwan University Master Thesis

• Main Authors: Wei-yu Cao, 曹維雨
• Other Authors: Feng-Li Lian
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/39708236984568525050

碩士 === 國立臺灣大學 === 電機工程學研究所 === 105 === In recent years, unmanned aerial vehicle is getting more and more popular, for its convenient use and stable performance. People can take photos or videos with it at somewhere they cannot get. However, when there is only one person, and he wants to record the experience at that time, it will become more difficult. This thesis will try to solve this problem. The first problem is to find the person, which is the pedestrian detection system in this thesis. The system uses deformable part model with HOG-SVM method, which can detect each part of the human body. By the reference of the standard statistics of human proportion, the corresponding parts can be matched if they belong to the same person. Hard examples which are generated from negative datasets are used to improve the accuracy of the detection. Several algorithms including detection rate and stability are introduced using deformable part model, and some optimization method can further improve the performance of the output. With several feedback mechanisms, the detection system can detect pedestrians with great accuracy. After all the pedestrians on the input image are detected, a pedestrian tracking system is proposed to make the quad-rotor follow the target. An enhanced SURF key-point matching method is proposed to improve the matching quality, which can output more matching key-points and remove the obviously incorrect matching. The specific human can be tracked in static frame efficiently with a circulation region of interest, which is the output of the last frame to ensure the stability. Meanwhile, a feedback mechanism is introduced to make sure the system detects the pre-set target. Some lines on the screen are defined to give a reference of quad-rotor’s moving. If the target is out of these lines, the quad-rotor will move in corresponding direction, to ensure the target is in good position on the screen. While flying, the system will match with the 5 sides of the target’s key-points which are captured in advance, and compute the percentage of the front side among all sides. If the percentage is below a threshold, the quad-rotor will turn, and then fly to the front side of the target, to ensure the good tracking quality on the screen.