Multiple Faces Tracking via Statistical Appearance Model
Abstract
Recently, appearance based methods have become a dominating trend in tracking. For example, tracking-by-detection models a target with an appearance classifier that separates it from the surrounding background. Recent advances in multi-target tracking suggest learning an adaptive appearance affinity measurement for target association. In this paper, statistical appearance model (SAM), which characterizes facial appearance by its statistics, is developed as a novel multiple faces tracking method. A major advantage of SAM is that the statistics is a target-specific and scene-independent representation, which helps for further video annotation and behavior analysis. By sharing the statistical appearance models between different videos, we are able to improve tracking stability on quality-degraded videos.
Keywords
Tracking Appearance model LearningNotes
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 60974129) and Intramural Research foundation of NJUST (2011YBXM119).
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