One of the most labor intensive aspects of developing ac- curate visual object detectors using machine learning is to gather sufficient amount of labeled examples. We develop a selective sampling method, based on boosting, which dra- matically reduces the amount of human labor required for this task. We apply this method to the problem of detecting pedestrians from a video camera mounted on a moving car. We demonstrate how combining boosting and active learn- ing achieves high levels of detection accuracy in complex and variable backgrounds.
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