| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 107 === Human detection is an important task in various surveillance applications under the current development of video media. In recent years, due to the rapid learning of deep learning, there have been many studies on the detection of people based on deep learning. However, most of the research is on the image of the projective camera. On the contrary, the research under the top-view fisheye camera is relatively rare. Therefore, this paper sets it as the research target.
There are many different neural network architectures under deep learning. This paper uses the Mask-RCNN neural network architecture to detect people under the top-view fisheye camera. Since the people under the top-view fisheye camera will have different deformations at various positions in the picture, in order to increase the accuracy, the people under the fisheye camera are independently trained into one class, and this paper also divides the picture into central and periphery part, divided into two models for training, and then combine the results to compare with the former. In addition, many related experiments have been done, hoping to improve the detection rate.
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