In this work, a novel occlusion detection algorithm using online learning is proposed for video applications. Each frame of a video is considered as a time-step for which pixels are classified as being either occluded or non-occluded. The Hedge algorithm is employed to determine weights for a set of experts, each of which is tuned to detect a specific type of occlusion boundary. In contrast to previous training-based methods, the proposed algorithm does not require any training, and has a runtime linear with respect to the number of experts considered. Detection performance is excellent on novel video sequences for which training data does not exist. In addition, the proposed algorithm is easily extended to provide classification results supplementary to detection. We demonstrate results on a series of challenging video sequences including a dataset of hand-labelled occlusion boundaries.