A hierarchical clustering based non-maximum suppression method in pedestrian detection

Abstract

We learned that one true positive would have a cluster with dense detected windows near the geometric center of pedestrian, so we adopted clustering methods based on ellipse Euclidean distance to get the location of pedestrian. Moreover, considering the big-size pedestrians and small ones respond differently to the same classifier and a 'weak' true positive (few fire times) may be filtered, we partitioned the non-maximum suppression process into two parts to analyze them distinctively. We call this method hierarchical non-maximum suppression. The experiment showed that our non-hierarchical clustering based method did well as proposed by Dalal and consumed much less time (nearly 100 fold less time at 150 magnitude windows), while the proposed hierarchical algorithm recalled more true positives than the non-hierarchical method (5% percent higher detection rate at FPPI = 1). © 2012 Springer-Verlag.