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【论文题目】A Spatio-temporal Deep Architecture for Surveillance Event Detection Based on ConvLSTM

【作    者】Kaihui Zhou, Yandong Zhu, Yanyun Zhao        点击下载PDF全文

【关 键 字】Event Detection, Key-pose, CNN, LSTM, Surveillance Video

【发表刊物/会议】
    Visual Communications and Image Processing (VCIP 2017)

【摘    要】
     Accurate event detection in surveillance videos is one of the most challenging tasks in computer vision since there is enormous noise produced by unwanted events. In this paper, we propose a method which concentrates on the target event by detecting person’s key-pose while combines the temporal information describing the key pose changes over time. Explicitly, we propose a recurrent model based on ConvLSTM integrated with temporal pooling (CLITP) to capture temporal representations as well as spatial features. In addition, our model can deal with variable-length sequences and work well on small datasets. And we conduct experiments on canonical surveillance event detection datasets, TRECVID SED dataset and multiple cameras fall dataset. Our method synthesizing both spatial and temporal information shows very competitive results compared with the state-of-the-art methods.

【发 表 年】2017

【发 表 月】10

【类    别】计算机视觉


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