Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble
This addresses safety and coordination in interconnected traffic systems for cyclists and vehicles, but it is incremental as it builds on existing sensor and ensemble methods.
The paper tackled the problem of detecting when cyclists start moving by combining a 3D CNN for image-based motion detection with smart device data, using a boosted stacking ensemble. The result was a fast and robust detector evaluated on real-world data from 49 subjects and 84 starting motions.
In future, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation on different levels, such as situation prediction or intention detection. In this article we present a cooperative approach for starting movement detection of cyclists using a boosted stacking ensemble approach realizing feature- and decision level cooperation. We introduce a novel method based on a 3D Convolutional Neural Network (CNN) to detect starting motions on image sequences by learning spatio-temporal features. The CNN is complemented by a smart device based starting movement detection originating from smart devices carried by the cyclist. Both model outputs are combined in a stacking ensemble approach using an extreme gradient boosting classifier resulting in a fast and yet robust cooperative starting movement detector. We evaluate our cooperative approach on real-world data originating from experiments with 49 test subjects consisting of 84 starting motions.