A Single Camera Unit-Based Three-Dimensional Surface Imaging Technique

The main objective of this study is to develop a single-camera unit-based three-dimensional surface imaging technique that could be used to reduce the disparity error in three-dimensional (3D) image reconstruction and simplify the calibration process of the imaging system. The current advanced stereoscopic 3D imaging system uses a pair of imaging devices (e.g., complementary metal-oxide semiconductor (CMOS) or charge-coupled device (CCD)), imaging lenses, and other accessories (e.g., light sources, polarizing filters) and diffusers.) To reconstruct the 3D scene, the system needs to calibrate the camera and compute a disparity map. However, in most cases in the industry, a pair of imaging devices is not ideally identical, so it is a necessary step to finely adjust and compensate for camera orientation, lens focal length, and intrinsic parameters for each camera. More importantly, conventional stereoscopic systems may respond differently to incident light reflected from the target surface. It is possible for the pixel information in the left and right images to be slightly different. This results in an increase in disparity error, even though the stereovision system is calibrated and compensated for rotation and vertical offsets between two cameras. This thesis aims to solve the aforementioned challenges by proposing a new stereo vision scheme based on only one camera to obtain target 3D data by 3D image reconstruction of two images obtained from two different camera positions

Golf Ball Detection and Tracking Based on Convolutional Neural Networks

With the rapid growth in artificial intelligence (AI), AI technologies have completely changed our lives. Especially in the sports field, AI starts to play the role in auxiliary training, data management, and systems that analyze training performance for athletes. Golf is one of the most popular sports in the world, which frequently utilize video analysis during training. Video analysis falls into the computer vision category. Computer vision is the field that benefited most during the AI revolution, especially the emerging of deep learning. This thesis focuses on the problem of real-time detection and tracking of a golf ball from video sequences. We introduce an efficient and effective solution by integrating object detection and a discrete Kalman model. For ball detection, five classical convolutional neural network based detection models are implemented, including Faster R-CNN, SSD, RefineDet, YOLOv3, and its lite version, YOLOv3 tiny. At the tracking stage, a discrete Kalman filter is employed to predict the location of the golf ball based on its previous observations. As a trade-off between the detection accuracy and detection time, we took advantage of image patches rather than the entire images for detection. In order to train the detection models and test the tracking algorithm, we collect and annotate a collection of golf ball dataset. Extensive experimental results are performed to demonstrate the effectiveness of the proposed technique and compare the performance of different neural network models