A distance metric learning based summarization system for nursery school surveillance video

In this paper, we present a system for summarizing nursery school surveillance video. The system takes full use of a learned distance metric, which can properly measure the similarity between videos. The metric is combined with supervised classification and unsupervised clustering, to categorize raw video materials into individual events. By selecting representative videos for each event, the system produces short video digests as the summarization output. The digests cover and reflect the children's activities on a daily basis. They are not only of interest to the parents, but also provide easy access to the mass quantity of daily surveillance video data. We implemented the proposed system in a real nursery school environment and confirmed its performance through both quantitative experiment and questionnaire survey.