Early terminating algorithms for Adaboost based detectors

In this paper we propose an early termination algorithm for speeding up the detection phase of the Adaboost based detectors. In the basic algorithm, at a specific search location, the AdaBoost ensemble response is computed as monotonic decreasing function of weak learners. As more weak learners are evaluated, the response either decreases or remains the same. As soon as the current response becomes lower than the AdaBoost global threshold, remaining computations may be skipped without any loss of accuracy. We further extend the basic algorithm by integrating it with the Non Maxima Suppression (NMS) process. Any candidate location may be discarded, as soon as its current response becomes lower than another candidate location, within the same non-maxima suppression window. In our experiments, our proposed algorithm has been found to be an order of magnitude faster than the traditionally used AdaBoost detector, for the application of edge-corner detection. Speedup comparisons are also done with other three well known edge corner detectors. The early terminated AdaBoost detector has been found to be significantly faster than all three of these detectors.