Finding People in Archive Films Through Tracking

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).