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Low complexity on-line video summarization with Gaussian mixture model based clustering

Techniques of video summarization have attracted significant research interests in the past decade due to the rapid progress in video recording, computation, and communication technologies. However, most of the existing methods analyze the video in an off-line manner, which greatly reduces the flexibility of the system. On-line summarization, which can progressively process video during video recording, is then proposed for a wide range of applications. In this paper, an on-line summarization method using Gaussian mixture model is proposed. As shown in the experiments, the proposed method outperforms other on-line methods in both summarization quality and computational efficiency. It can generate summarization with a shorter latency and much lower computation resource requirements.