A framework for unsupervised training of object detectors from unlabeled surveillance video

Object detection is a critical step in automated surveillance. A common approach to building object detectors involves statistical learning techniques using as input large annotated data sets. However, due to inevitable limitations of a typical training data set, this supervised approach is unsuitable for building a generic surveillance system applicable to a wide variety of scenes, objects and camera setups. To make a step towards a more generic object detection solution, we propose in this paper an unsupervised method capable of learning and detecting the dominant object class in a general dynamic scene observed by a static camera. In the first step of the method, a coarse object detector is built to identify candidate dominant objects based on motion segmentation results obtained for the observed scene. Then, clustering and cluster validation are applied to refine the output of the coarse detector and to select a subset of this output that can be used to train a more sophisticated dominant object detector. Finally, we deploy this trained detector to find further instances of the dominant object class in the observed scene. We demonstrate the effectiveness of our method experimentally on four representative video sequences.