| Summary: | 碩士 === 國立中正大學 === 資訊工程研究所 === 106 === With the proliferation of deep learning techniques, a significant amount of applications related to home caring systems emerge recently. In particular, detecting abnormal events in a smart home environment has become more mature. According to recent statistics, the most common injury in the elderly population is falling. Existing approaches have mostly conducted intelligence video analysis of single camera for two cases of fall and non-fall. In this paper, we adopt deep learning techniques including convolutional neural networks (CNN) and long short-term memory (LSTM) to construct deep networks for human behavior recognition in a multi-view framework. We set up our experimental environment as a normal residence to collect a large amount of data, and falling is one of the actions included in our dataset. It is not just identifying either fall or non-fall. Our model can identify six human behaviors in total, namely walking, falling, lying down, climbing up, bending, and sitting down. Additionally, we use two cameras as our sensors to efficiently overcome the problem of blind angles and improve performance based on the multi-view setting. After performing a series of image preprocessing in the raw data, we obtain the human silhouette images as the input to our training model. In addition, because the real-world datasets are complicated for analyzing and understanding, the assignment of labeling data is time-consuming and money-consuming. Therefore, we present image clustering based on stacked convolutional auto-encoder (SCAE) which applies the clustering labels to replace the manual labels for auto-labeling. Finally, the experimental results demonstrate that the performance and novelty of our proposed framework.
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