A Part-Aware Multi-Scale Fully Convolutional Network for Pedestrian Detection

Pedestrian detection is a crucial task in intelligent transportation systems, which can be applied in autonomous vehicles and traffic scene video surveillance systems. The past few years have witnessed much progress on the research of pedestrian detection methods, especially through the successful use of the deep learning based techniques. However, occlusion and large scale variation remain the challenging issues for pedestrian detection. In this work, we propose a Part-Aware Multi-Scale Fully Convolutional Network (PAMS-FCN) to tackle these difficulties. Specifically, we present a part-aware Region-of-Interest (RoI) pooling module to mine body parts with different responses, and select the part with the strongest response via voting. As such, a partially visible pedestrian instance can receive a high detection confidence score, making it less likely to become a missing detection. This module operates in parallel with an instance RoI pooling module to combine local parts and global context information. To handle vast scale variation, we construct a fully convolutional network in which multi-scale feature maps are generated efficiently, and small-scale and large-scale pedestrians are detected separately. By integrating these structures, the proposed detector achieves the state-of-the-art performance on the Caltech, KITTI, INRIA and ETH pedestrian detection datasets.

Automated summary

Pedestrian detection is a crucial task in intelligent transportation systems, and recent progress has been made through the use of deep learning techniques. This work proposes a Part-Aware Multi-Scale Fully Convolutional Network (PAMS-FCN) to address challenges such as occlusion and scale variation in pedestrian detection. The proposed detector achieves state-of-the-art performance on various datasets, demonstrating its effectiveness in handling these issues.