A Context Aware and Video-Based Risk Descriptor for Cyclists
Monitoring cyclists’ data is a keystone to foster urban cyclists’ safety by helping urban planners to design safer cyclist routes. In this work, we propose a fully image-based framework to assess the route risk from the cyclist perspective. From smartphone sequences of images, we are able to automatically identify events considering different risk criteria based on the cyclist’s motion and object detection. This method provides context on the situation and is independent from the expertise level of the cyclist. From the inertial sensor data, we automatically detect the route segments performed by bicycle, applying behavior analysis techniques. We test our methods on real data, attaining very promising results in terms of risk classification and behavior analysis accuracy.