Automatic Vehicle Following A Specific Moving Objec

• Main Authors: Wang, Te-Yi, 王得懿
• Other Authors: Tseng, Din-Chang
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/00301043676290294031

碩士 === 國立中央大學 === 資訊工程學系在職專班 === 102 === In this thesis, we propose a automatic vehicle system, which can track the specified moving object. Use the real-time video and target detection and location technology, then the automatic vehicle can achieve active video tracking by image and follow this moving object control. This main system is integrated by two parts. One is the architecture of the hardware and the other is software flow control with image processing algorithms. The architecture of the hardware could be implemented by the four sub-systems: There are MCU system, image processing system, the power driving system, and the communication system. Each sub-system has individual hardware, interface, and peripheral. The main system operates by all sub-systems, performs image processing algorithm of MCU, and controls all peripherals by interface. For example, the MCU controls the camera module of image acquisition, send direction signals to the motor controller, and communicates with remote PC, etc. Software flow control and algorithms can be implemented by three parts: The first one is Object detection and location, the second is object tracking, and the last is the motion control of automatic vehicle. The Object detection and location utilizes color detection, which define the range of colors by Gaussian Mixture Model (GMM) and transform to YCbCr color-space for processing, the YCbCr color-space is lighting-insensitivity. After the YCbCr color-space transformation, we can get the feature from target object image by thresholding, morphology noise removal, and contour searching method, and furthermore the object tracking obtains a look-up table by calibration method. This calibration method establishes a transform between image’s pixels and real world’s distance. This distance can be used to estimate the target is in left or right relative to camera center line. Following that, we use trigonometric function to evaluate the hypotenuse and acute angle of the right triangle, then to control the driver of the automatic vehicle going forward, rotation, and stop. Finally, in this research, by the integration of software algorithm and hardware device that we use the image feature distance measurement method to approach controlling the automatic vehicle that can follow the specified object.