Scale-Adaptive Deconvolutional Regression Network for Pedestrian Detection

Although the Region-based Convolutional Neural Network (R-CNN) families have shown promising results for object detection, they still face great challenges for task-specific detection, e. g., pedestrian detection, the current difficulties of which mainly lie in the large scale variations of pedestrians and insufficient discriminative power of pedestrian features. To overcome these difficulties, we propose a novel ScaleAdaptive Deconvolutional Regression (SADR) network in this paper. Specifically, the proposed network can effectively detect pedestrians of various scales by flexibly choosing which feature layer to regress object locations according to the height of pedestrians, thus improving the detection accuracy significantly. Furthermore, considering CNN can abstract different semantic-level features from different layers, we fuse features from multiple layers to provide both local characteristics and global semantic information of the object for final pedestrian classification, which improves the discriminative power of pedestrian features and boosts the detection performance further. Extensive experiments have verified the effectiveness of our proposed approach, which achieves the state-of-the-art log-average miss rate (MR) of 6.94% on the revised Caltech [1] and a competitive result on KITTI.