On-line conservative learning for person detection
2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), 2005
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 exploit a huge amount of unlabeled video data by being very conservative in selecting training examples and to start with a very simple object detection system and using 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.