Title :
Finding people in archive films through tracking
Author_Institution :
Toyota Technol. Inst. at Chicago, Chicago, IL
Abstract :
The goal of this work is to find all people in archive films. Challenges include low image quality, motion blur, partial occlusion, non-standard poses and crowded scenes. We base our approach on face detection and take a tracking/temporal approach to detection. Our tracker operates in two modes, following face detections whenever possible, switching to low-level tracking if face detection fails. With temporal correspondences established by tracking, we formulate detection as an inference problem in one-dimensional chains/tracks. We use a conditional random field model to integrate information across frames and to re-score tentative detections in tracks. Quantitative evaluations on full-length films show that the CRF-based temporal detector greatly improves face detection, increasing precision for about 30% (suppressing isolated false positives) and at the same time boosting recall for over 10% (recovering difficult cases where face detectors fail).
Keywords :
face recognition; image motion analysis; inference mechanisms; CRF; archive films; crowded scenes; face detection; inference problem; low image quality; motion blur; nonstandard poses; partial occlusion; people tracking; Boosting; Clothing; Computer vision; Detectors; Face detection; Image quality; Layout; Lighting; Switches; Visualization;
Conference_Titel :
Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4244-2242-5
Electronic_ISBN :
1063-6919
DOI :
10.1109/CVPR.2008.4587533