On the safety of vulnerable road users by cyclist detection and tracking

Abstract

Timely detection of vulnerable road users is of great relevance to avoid accidents in the context of intelligent transportation systems. In this work, detection and tracking is acknowledged for a particularly vulnerable class of road users, the cyclists. We present a performance comparison between the main deep learning-based algorithms reported in the literature for object detection, such as SSD, Faster R-CNN and R-FCN along with InceptionV2, ResNet50, ResNet101, Mobilenet V2 feature extractors. In order to identify the cyclist heading and predict its intentions, we propose a multi-class detection with eight classes according to orientations. To do so, we introduce a new dataset called “CIMAT-Cyclist”, containing 20,229 cyclist instances over 11,103 images, labeled based on the cyclist’s orientation. To improve the performance in cyclists’ detection, the Kalman filter is used for tracking, coupled together with the Kuhn–Munkres algorithm for multi-target association. Finally, the vulnerability of the cyclists is evaluated for each instance in the field of view, taking into account their proximity and predicted intentions according to their heading angle, and a risk level is assigned to each cyclist. Experimental results validate the proposed strategy in real scenarios, showing good performance.