Uncovering Activity and Patterns in Video using Slit-Tear Visualizations

In prior work, we introduced a visualization technique for analyzing fixed position video streams called slit-tear visualizations. This technique supports exploratory data analysis by interactively generating views about the video stream that can provide insight into the spatial/temporal relationships of the entities contained within. These insights are necessarily grounded in context of the specific video being analyzed, and in this paper, we provide a general typology of the kinds of slit-tears an analyst may use. Further, we discuss the kinds of analytic primitives that often signal relevant events given these slit-tear types. The work is relevant to human-centered computing because the technique provides the most insight in the presence of human interpretation.