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• W1489207701 abstract "We present a novel on-line conservative learning framework for an object detection system. All algorithms operate in an on-line mode, in particular we also present a novel on-line AdaBoost method. The basic idea is to start with a very simple object detection system and to exploit a huge amount of unlabeled video data by being very conservative in selecting training examples. The key idea is to use reconstructive and discriminative classifiers in an iterative co-training fashion to arrive at increasingly better object detectors. We demonstrate the framework on a surveillance task where we learn person detectors that are tested on two surveillance video sequences. We start with a simple moving object classifier and proceed with incremental PCA (on shape and appearance) as a reconstructive classifier, which in turn generates a training set for a discriminative on-line AdaBoost classifier" @default.
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• W1489207701 date "2005-01-01" @default.
• W1489207701 modified "2023-10-18" @default.
• W1489207701 title "On-line Conservative Learning for Person Detection" @default.
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• W1489207701 doi "https://doi.org/10.1109/vspets.2005.1570919" @default.
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