Cascaded online boosting

In this paper, we propose a cascaded version of the online boosting algorithm to speed-up the execution time and guarantee real-time performance even when employing a large number of classifiers. This is the case for target tracking purposes in computer vision applications. We thus revise the on-line boosting framework by building on-the-fly a cascade of classifiers dynamically for each new frame. The procedure takes into account both the error and the computational requirements of the available features and populates the levels of the cascade accordingly to optimize the detection rate while retaining real-time performance. We demonstrate the effectiveness of our approach on standard datasets. Keywords Online Boosting, Multiple classifiers systems, Object detection, Tracking

• Type: Journal
• Year: 2010
• Where: JRTIP
• Authors: Ingrid Visentini, Lauro Snidaro, Gian Luca Foresti
• Keywords: Boosting, JRTIP 2010, On-line Boosting Framework, Real-time Performance