title: An online learning approach to occlusion boundary detection.
abstract: We propose a novel online learning-based framework for occlusion boundary detection in video sequences. This approach does not require any prior training and instead "learns" occlusion boundaries by updating a set of weights for the online learning Hedge algorithm at each frame instance. Whereas previous training-based methods perform well only on data similar to the trained examples, the proposed method is well suited for any video sequence. We demonstrate the performance of the proposed detector both for the CMU data set, which includes hand-labeled occlusion boundaries, and for a novel video sequence. In addition to occlusion boundary detection, the proposed algorithm is capable of classifying occlusion boundaries by angle and by whether the occluding object is covering or uncovering the background.
authors.affiliation: Department of Electrical and Computer Engineering, University of California at San Diego, La Jolla, CA 92093, USA. njacobso@ucsd.edu
authors.forename: Natan
authors.initials: N
authors.lastname: Jacobson
authors.forename: Yoav
authors.initials: Y
authors.lastname: Freund
authors.forename: Truong Q
authors.initials: TQ
authors.lastname: Nguyen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014741:Video Recording / Q000379:methods*