Enhancing sketch-based sport video retrieval by suggesting relevant motion paths

Searching for scenes in sport videos is a task that recurs very often in game analysis done by coaches and other related activities. In most cases, queries are formulated on the basis of specific motion characteristics the user remembers from the video. Providing sketching interfaces for graphically specifying query input is thus a very natural user interaction for a retrieval application. However, the quality of the query (the sketch) heavily depends on the memory of the user of and her ability to accurately formulate the intended search query by transforming this 3D memory of the known item(s) into 2D sketch query. In this paper, we present an auto-suggest search feature that harnesses spatiotemporal data of sport videos to suggest potential directions containing relevant data during the formulation of a sketch-based motion query. Users can intuitively select the direction of the desired motion query on-the-fly using the displayed visual clues, thus relaxing the need for relying heavily on memory to formulate the query, and at the same time significantly enhancing the accuracy of the results and the speed at which they appear. A first evaluation has shown the effectiveness and efficiency of our approach.