independently. By coupling both detection and estimation it is possible to incrementally learn models to face both problems. This a novel framework proposing a joint detection and recognition of both head and body poses. The framework is based on learning an ensemble of pose-sensitive human body models whose outputs provide a new representation for poses. This type of work avoids tedious and inconsistent manual annotation for learning pose-sensitive models. Consequently, I formulated a semi-supervised learning method for model training which bootstraps an initial model using a small set of labelled data, and subsequently improves the model iteratively by data mining from a large unlabelled dataset. Experiments in CCTV videos from a busy station of the London Underground demonstrate that the proposed method significantly outperforms a state-of-the-art person detector and is able to yield extremely accurate head and body pose estimation in crowded public spaces. |
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