Aerial Surveillance on Ground Object Detection

Abstract

The main surveillance methods are divided into two section ground surveillance and aerial surveillance Ground surveillance has disadvantages to subject to environmental constraint and smaller detection range to be more time consuming Aerial surveillance is more suitable for complex environments and covers a much larger spatial area This thesis develops an aerial surveillance on ground object detection using vehicle detection The proposed system is constructed based on machine learning algorithm which involves training process and testing process Sample collection is the most important part of object detection Wind screen and lamps are the main characteristics chosen to recognize a vehicle in the proposed AdaBoost vehicle detection system In this thesis it trains samples uses multiple train cascaded LBP classifier Through the image process it can improve the detection rate false alarm rate and process speed The experiment results show the propose system can run all day on a dynamic platform with constant attitude to detect various vehicles The proposed system can be applied on the UAV in constant altitude autopilot as well as any real-time surveillance applications

Date of Award	2015 Aug 18
Original language	English
Supervisor	Chin-E. Lin (Supervisor)