Monocular Pedestrian Orientation Recognition Based on Capsule Network for a Novel Collision Warning System
Safaâ Dafrallah, Aouatif Amine, Stéphane Mousset, Abdelaziz Bensrhair · IEEE Access · 2021
Pedestrians are the most vulnerable road users, with around 23% of world road traffic fatalities. To prevent such traffic collisions, the Pedestrian Collision Warning System (PCWS) alerts the driver before an imminent collision. In order to protect worldwide pedestrians, the PCWS should take into account different pedestrian crossing behaviors and different road structures, especially pedestrians with risky behavior on unstructured environments. Since oblique crossing is the usual crossing way of pedestrians with risky behaviors, recognizing the pedestrian walking direction is one of the key factors to consider. However, the existent PCWS focuses more on detecting pedestrians rather than recognizing their walking direction. This paper highlights the safety of different kinds of pedestrians by presenting a novel approach used to estimate pedestrian’s orientation from a single-frame. Next, the estimated orientation is integrated into the proposed PCWS system. Our method involves Capsule Network based technique to train on the pedestrian images. For this purpose, a new pedestrian orientation dataset taken from real city-scenes named SafeRoad was created, using a single camera mounted on a moving vehicle. TUD Multiview Pedestrian and Daimler datasets are thereafter used as a benchmark to evaluate the proposed approach. Experimental results show that Capsule Network exceeds significantly the accuracy performance on the three datasets compared to Convolutional Neural Networks algorithms.