Unsupervised and Online Update of Boosted Temporal Models: The UAL2Boost

The application of learning-based vision techniques to real scenarios usually requires a tunning procedure, which involves the acquisition and labeling of new data and in situ experiments in order to adapt the learning algorithm to each scenario. We address an automatic update procedure of the L₂boost algorithm that is able to adapt the initial models learned off-line. Our method is named UAL₂Boost and present three new contributions: (i) an on-line and continuous procedure that updates recursively the current classifier, reducing the storage constraints, (ii) a probabilistic unsupervised update that eliminates the necessity of labeled data in order to adapt the classifier and (iii) a multi-class adaptation method. We show the applicability of the on-line unsupervised adaptation to human action recognition and demonstrate that the system is able to automatically update the parameters of the L₂boost with linear temporal models, thus improving the output of the models learned off-line on new video sequences, in a recursive and continuous way. The automatic adaptation of UAL₂Boost follows the idea of adapting the classifier incrementally: from simple to complex.