Point-placement techniques and Temporal Self-similarity Maps for Visual Analysis of Surveillance Videos

The manual analysis of surveillance videos is unfeasible due to the excessive amount of data, the associated subjectivity, or the eventual presence of distracting noise. Automatic summarization approaches provide little/no user interaction, limiting his/her comprehension regarding the involved phenomena. Visual analytics techniques represent a potential tool for such analysis, providing video representations that clearly communicate their content, potentially revealing patterns that may represent events of interest. In this paper, we present a methodology for visual analysis of surveillance video that combines point-placement techniques and Temporal Self-similarity Maps (TSSMs) to reveal the events occurrence structure and to enhance the comprehension of their temporal properties. Experiments in several surveillance scenarios demonstrate that our proposed methodology provides an effective events summarization and the exploration of both the structure of each event and the relationship among them, allowing the security agent to filter/explore those that represent potential alert situations.