A Novel Internet-of-Vehicles Assisted Collaborative Low-visible Pedestrian Detection Approach
Peng Sun, Azzedine Boukerche · 2020
For releasing the public concern on road safety, as an essential driving assistant technique for supporting autonomous deriving, considerable research efforts have been paid on developing practical traffic-related target/object detection methods. In recent years, by exploiting the powerful parallel processing capability of GPU and the feature extraction ability of deep convolutional neural network (CNN), the visible light image-based pedestrian detection method has gradually been considered as a potential solution. However, although it has been proven in the existing literature that CNN-based pedestrian detection methods can greatly improve the detection efficiency for lightly occluded pedestrians, the detection of low-visible pedestrians is still an open challenge. Accordingly, in this paper, we propose a novel collaborative pedestrian detection frame based on the Internet-of-Vehicles (IoV) to detect low-visible/hidden pedestrians or even hidden pedestrians. We further evaluate the proposed pedestrian detection framework relying on simulation experiments.