A detector tree of boosted classifiers for real-time object detection and tracking
This paper presents a novel tree classifier for complex object detection tasks together with a general framework for real-time object tracking in videos using the novel tree classifier. A boosted training algorithm with a clustering-and-splitting step is employed to construct branches in the nodes recursively, if and only if it improves the discriminative power compared to a single monolithic node classifier and has a lower computational complexity. A mouth tracking system that integrates the tree classifier under the proposed framework is built and tested on XM2FDB database. Experimental results show that the detection accuracy is equal or better than a single or multiple cascade classifier, while being computational less demanding.