  As sensing technology proliferates and becomes affordable to the general
public, there is a growing trend in citizen science where scientists and
volunteers form a strong partnership in conducting scientific research
including problem finding, data collection, analysis, visualization, and
storytelling. Providing easy-to-use computational tools to support citizen
science has become an important issue. To raise the public awareness of
environmental science and improve the air quality in local areas, we are
currently collaborating with a local community in monitoring and documenting
fugitive emissions from a coke refinery. We have helped the community members
build a live camera system which captures and visualizes high resolution
timelapse imagery starting from November 2014. However, searching and
documenting smoke emissions manually from all video frames requires manpower
and takes an impractical investment of time. This paper describes a software
tool which integrates four features: (1) an algorithm based on change detection
and texture segmentation for identifying smoke emissions; (2) an interactive
timeline visualization providing indicators for seeking to interesting events;
(3) an autonomous fast-forwarding mode for skipping uninteresting timelapse
frames; and (4) a collection of animated smoke images generated automatically
according to the algorithm for documentation, presentation, storytelling, and
sharing. With the help of this tool, citizen scientists can now focus on the
content of the story instead of time-consuming and laborious works.
