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The purpose of this study is to stimulate communication among the elderly population of Japan (where more than 21% of the population is aged 65 years and above) and to find ways to use their knowledge and labor skills with the aid of information and communications technology. Toward this end, we launched a joint research project called "Senior Cloud" in which we developed a prototype of a social TV system for the elderly. The first phase of the research project included a threemonth field trial using the system in a local senior community. In this paper, we analyze the system log (posting and operational data) and the responses to questionnaires administered to obtain user impressions. These data reveal certain characteristics of communication among the elderly and methods that can stimulate it.

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There is a need for systems that can automatically detect specific human actions in a surveillance video. However, almost all of the human action recognition techniques proposed so far are for detecting relatively large actions within simple video sequences. To alleviate this shortcoming, we propose a method that can detect specific actions within crowd sequences of real surveillance video. Our action recognition method is based on the bag-of-features approach, and key-point trajectories are used as its features. One problem is that key-point trajectories cannot be directly input when using the bag-of-features approach, because they have various time lengths. To overcome this difficulty, our method extracts a fixed-length feature descriptor from a key-point trajectory and uses it for event classification. In addition, feature weights are calculated for reducing the interference from noise trajectories in the background regions. Our method could more precisely detect specific actions than conventional other methods, and it performed well in the TRECVID 2010 Surveillance Event Detection task.