Accuracy prediction for pedestrian detection

Abstract

In this paper, we address the problem of predicting accuracy for pedestrian detection. We want to be able to predict the accuracy of a video analytic method without actually executing the method. We propose the use of texture descriptors and random forests to predict the accuracy of various pedestrian detection methods. Our experimental results demonstrate that using the local binary pattern (LBP) or a bank of Schmid and Gabor filters can capture spatial textural information associated with video quality degradation that can be used to predict accuracy. We also demonstrate how predicting the absolute accuracy can save network and computational resources.