Algorithmic optimisation of histogram intersection kernel support vector machine-based pedestrian detection using low complexity features

Histogram intersection kernel support vector machine (SVM) is accepted as a better discriminator than its linear counterpart when used for pedestrian detection in images and video frames. Its computational complexity has, however, limited its use in practical real-time detectors. To circumvent this problem, prior work proposed a low complexity detection framework based on integer-only histograms of oriented gradient features which allow a look-up table-based implementation of kernel SVM leading to further simplification without compromising detection performance. This work describes several important enhancements made in the original framework related to the pre-processing steps, feature calculation and training setup. Resultantly, the augmented framework, proposed in this study, stands out in terms of the detection accuracy and computational complexity compared to contemporary detectors. The best detector described in this study achieves 8 and 2% lesser miss rates (MRs) on ETH and INRIA pedestrian datasets, respectively, compared to the well-known boosting cascades-based aggregate channel feature detector despite avoiding complex floating point operations. Moreover, the proposed detector performs exceptionally better in scenarios where less than 10(-2) false positives per image are desired as demonstrated through the MR versus false positive curves.