Aiming to reduce pollutant emissions, bicycles are regaining popularity specially in urban areas. However, the number of cyclists' fatalities is not showing the same decreasing trend as the other traffic groups. Hence, monitoring cyclists' data appears as 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 rout risk from the cyclist perspective. From smartphone sequences of images, this generic framework is able to automatically identify events considering different risk criteria based on the cyclist's motion and object detection. Moreover, since it is entirely based on images, our method provides context on the situation and is independent from the expertise level of the cyclist. Additionally, we build on an existing platform and introduce several improvements on its mobile app to acquire smartphone sensor data, including video. 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, according to two different criteria, and behavior analysis accuracy.
@cite_7: Due to economic and environmental issues, bicycles have been regaining their significance as a transportation vehicle in urban scenarios. To further drive this desirable trend, policy makers must have the tools to access current bicycle infrastructures and road safety concerns. Fundamental for this assessment is a deeper understanding of how cyclists use current infrastructures, if the cycling experience results in stressful events, and the conditions of the current infrastructure. We here introduce a new platform, SMARTcycling, that, by taking advantage of the mobile power available to a smartphone, captures and stores data from several sensors, namely an action camera, a cardio signal acquisition belt, and smartphone's Global Positioning System (GPS) coordinates. The data is further processed and, through visual cues, we access the cyclist driving events and road condition cues. SMARTcycling also detects the cyclist stress using the electrocardiograms (ECG) from the belt. We further contribute by making available a dataset containing the sensors data from 10 paths over two cities in Portugal. On this dataset, we show our initial promising results on event detection, road condition identification and stress assessment.
In this work we delve into more involved computer vision and image processing techniques to be able to automatically identify and contextualize dangerous events from external factors, sparing both the action camera and the bio-metric belt, which imply a more complex set-up. The descriptor used in @cite_7 was context independent (splitting the image into fixed zones), we now use a different and richer approach that encodes the context surrounding the cyclist when performing event detection. Moreover, contrary to our previous work, we analyze the whole image, incorporating motion, temporal dependence and image semantics.