We present a fast technique for retrieving video clips using free-hand sketched queries. Visual keypoints within each video are detected and tracked to form short trajectories, which are clustered to form a set of spacetime tokens summarising video content. A Viterbi process matches a space-time graph of tokens to a description of colour and motion extracted from the query sketch. Inaccuracies in the sketched query are ameliorated by computing path cost using a Levenshtein (edit) distance. We evaluate over datasets of sports footage.
Hu et. al. REF track SIFT keypoints to form short trajectories which are clustered to form a set of space-time tokens.
Motion-sketch Based Video Retrieval Using a Trellis Levenshtein Distance
2010 20th International Conference on Pattern Recognition
2010 20th International Conference on Pattern Recognition
Computer Science