Querying Videos Using DNN Generated Labels - Wu, Yifan; Drucker, Steven; Philipose, Matthai; Sivalingam, Lenin Ravindranath (HILDA’18: Workshop on Human-In-the-Loop Data Analytics, 2019)
Massive amounts of videos are generated for entertainment, security, and science, powered by a growing supply of user-produced video hosting services. Unfortunately, searching for videos is difficult due to the lack of content annotations. Recent breakthroughs in image labeling with deep neural networks (DNNs) create a unique opportunity to address this problem. While many automated end-to-end solutions have been developed, such as natural language queries, we take on a different perspective: to leverage both the development of algorithms and human capabilities. To this end, we design a query language in tandem with a user interface to help users quickly identify segments of interest from the video based on labels and corresponding bounding boxes. We combine techniques from the database and information visualization communities to help the user make sense of the object labels in spite of errors and inconsistencies.