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Boosted Tracking in Video
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AI
DOI：10.1109/LSP.2009.2030862.png)
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摘要
En 中文
We discuss how a probabilistic interpretation of the output provided by a cascade of boosted classifiers can be exploited for Bayesian tracking in video streams. In particular, real-time face and body detection can be achieved by relying on such a Bayesian framework. Results show that such integrated approach is appealing with respect both to robustness and computational efficiency.
Keyword:
Boosted classifiers
face detection
object tracking
particle filtering
pedestrian detection
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期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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