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IMPROVED FACE TRACKING THANKS TO LOCAL FEATURES CORRESPONDENCE

IMPROVED FACE TRACKING THANKS TO LOCAL FEATURES CORRESPONDENCE. Alberto Piacenza, Fabrizio Guerrini, RiccardoLeonardi. Department of Engineering Information – University of Brescia, Italy. OVERVIEW. FACE TRACKING ENHANCEMENT. MOTIVATION. SPECIFIC CHALLENGE. Semantic description of the

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IMPROVED FACE TRACKING THANKS TO LOCAL FEATURES CORRESPONDENCE

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  1. IMPROVED FACE TRACKING THANKS TO LOCAL FEATURES CORRESPONDENCE Alberto Piacenza, Fabrizio Guerrini, RiccardoLeonardi Department of Engineering Information – University of Brescia, Italy OVERVIEW FACE TRACKING ENHANCEMENT MOTIVATION SPECIFIC CHALLENGE Semantic description of the content in the Interactive Movietelling system [1] Identify the frames in which a main character is present BASELINE SOLUTION Use off-the-shelf tools for: face detection face recognition on the detected faces EFFECTS PROBLEMS SOLUTION Imprecise or missed face detection Face bounding box drifting Character recognition is unreliable Apply the face track enhancement stage Flowchart of the operations involved in the creation of the enhanced face tracks. Output tracks: the frames of a small excerpt of one shot are presented. Blue rectangles: detected faces correctly identified in a given face track. but the face detection has failed to find the face in the in-between frames. Green rectangles: recovered faces for in-between frames thanks to the face tracks enhancement process. • Re-extract POI in the bounding box • Use KLT tracker to the next frame • Estimate the new bounding box using RANSAC • Use backward tracking as well EXPERIMENTAL RESULTS ADDITIONAL INFO : number of ground-truth objects in frame : number of detected objects in frame • Interactive Movietelling system: • Reference: • [1] A. Piacenza, F. Guerrini, N. Adami, R. Leonardi, • J. Porteous, J. Teutenberg, M. Cavazza, “Generating • Story Variants with Constrained Video Recombination”, • 19th ACM Multimedia, pp. 223-232, 2011. • Link to example output clips: • www.ing.unibs.it/alberto.piacenza/TrackWithPoints : -th ground-truth object : -th detected object Frame Detection Accuracy (FDA): Acknowledgements: This work has been funded (in part) by the EC under grant Agreement IRIS (FP7-ICT-231824). Dataset of YouTube shots with faces (average: 181 frames) Comparison with the CAMSHIFT algorithm

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