Context-driven clustering by multi-class classification in an active learning framework

Tracking and detection of objects often require to apply complex models to cope with the large intra-class variability of the foreground as well as the background class. In this work, we reduce the complexity of a binary classification problem by a context-driven approach. The main idea is to use a hidden multi-class representation to capture multi-modalities in the data finally providing a binary classifier. We introduce virtual classes generated by a context-driven clustering, which are updated using an active learning strategy. By further using an on-line learner the classifier can easily be adapted to changing environmental conditions. Moreover, by adding additional virtual classes more complex scenarios can be handled. We demonstrate the approach for tracking as well as detection on different scenarios reaching state-of-the-art results.