Small-Scale Pedestrian Detection Based on Deep Neural Network

Abstract

Pedestrian detection is a crucial component for intelligent transport system and advanced driver assistance system. In recent years, pedestrian detection methods have achieved higher accuracy. However, the existing algorithms are insufficient for small-scale pedestrian detection that is relatively far from cameras in practical applications. In this paper, we propose a novel deep small-scale sense network (termed SSN) for small-scale pedestrian detection. The proposed architecture could generate some proposal regions which are more effective to detect small-scale pedestrians. Furthermore, we design a novel loss function based on cross entropy loss to increase the loss contribution from hard-to-detect small-scale pedestrians. In addition, a novel evaluation metric is introduced, which can measure the location precision of the pedestrian detection methods. In addition an Asian pedestrian detection dataset named VIP pedestrian dataset is constructed from various road condition data. Our method achieves good detection performance on Caltech pedestrian dataset and our VIP pedestrian dataset.