| Summary: | 碩士 === 國立交通大學 === 影像與生醫光電研究所 === 101 === Embedded vision spans a lot of markets and applications such as industrial, medical, automotive, security and consumer. Security surveillance is a significant application of embedded vision. Object detection is the first step to achieve this purpose. Existing software implementation of real-time object detection is based on small size images, favorable conditions in images. Mostly present hardware implementations are specific application, classifiers decreasing or images downscaling for achieving real-time processing. Therefore, it is necessary to design architecture that is capable to detect different objects under different scenarios in high resolution images. In this thesis, we propose cell-based parallel architecture permits each cell with parallel and independent execution. By exploiting this method, we accelerate the execute time, utilize fewer downscale images and more upscale features. The architecture is implemented by AdaBoost algorithm, verified by face detection utilizing MTI+CMU database, and synthesis by Xilinx ISE○R targets on Xilinx○R Zynq7000 XC7020. The proposed architecture achieves approximately 130 fps from receiving input image pixel by pixel to signing the candidate region. As result, we reserve area and time for applying multi-objects detection, and verify the architecture have ability to detect face in real time.
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