Training Object Detection Models with Weakly Labeled Data
Appearance based object detection systems utilizing statistical models to capture real world variations in appearance have been shown to exhibit good detection performance. The parameters of these statistical models are typically learned automatically from labeled training images. This process can be difficult in that a large number of labeled training examples may be needed to accurately model appearance variation. In this work we describe a method whereby a training set consisting of a small number of fully labeled training examples augmented with a set of weakly labeled examples can be used to train a detector which exhibits performance better than that which can be obtained with a reduced set of fully labeled training examples alone.