| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 107 === Recognizing the potential data value of the videos generated from ubiqui- tous personal devices (e.g., dash cams or smartphones), a video collection and analysis platform based on edge/fog and cloud computing is proposed to col- lect and utilize those videos effectively. However, for such a platform, due to the privacy and bandwidth issue, not all of the videos can transmit back to the cloud. We want to fully utilize these left data to increase the model performance further. Thus, in this thesis, we propose a novel distributed ar- chitecture for edge learning, which adopts semi-supervised techniques. The proposed system not only prevents from uploading the sensitive data but also reduce the communication cost. We then evaluate the performance of the sys- tem using real-world video data with a discussion on the performance impact of the hardware limitation at the edge.
|
|---|
