Fast Pedestrian Detection With Attention-Enhanced Multi-Scale RPN and Soft-Cascaded Decision Trees

Pedestrian detection has attracted more attention in the fields of computer vision and artificial intelligence. A variety of real-world applications involving pedestrian detection have been promoted, such as Advanced Driving Assistant System (ADAS). Although both two-stage and single-stage deeply learned object detectors have shown outstanding performance for general object detection, they are still facing the problem of poor accuracy in single-class detection senario because they are designed to distinguish objects from different categories rather than pay attention to various appearances of pedestrians. Previous leading pedestrian detectors F-DNN and F-DNN v2 fuse several neural networks like SSD, VGG16 and GoogLeNet to generate ROIs and supress false alarms with cascaded structure, resulting in low miss rate but high complexity. In this paper we propose a novel framework called Attention-Enhanced Multi-Scale Region Proposal Network (AEMS-RPN) for ROI generation, which also acts as first-stage classification. Inspired by the success of traditional pedestrian detectors, we use soft-cascaded decision trees instead of cascaded deep neural networks to achieve high accuracy and fast detection speed simultaneously. The decision tree classifier is used and enables us to combine features from different layers with various resolutions for classification and incorporate effective bootstrapping for mining hard negatives. We test our method on several pedestrian detection datasets and the experimental results certify the effectiveness of the proposed AEMS-RPN. Compared with the state-of-the-art, we obtain the competitive accuracy with near real-time efficiency.