Traffic intersection monitoring using fusion of GMM-based deep learning classification and geometric warping
Zahra Moayed, Anthony Griffin, Reinhard Klette · 2017
In this work we present a vision-based road user monitoring system for traffic intersections using a combination of Gaussian Mixture Model (GMM)-based deep learning approaches and geometric warping for further behaviour analysis. GMMs and detection of features in consecutive frames are used in bounding box prediction in order to track objects, while a Fast Regional Convolutional Neural Networks (R-CNN) approach classifies different road users. For better analysis, by knowing the actual coordinates of the intersection, we use geometric warping to map the 3D-plane to the 2D plane. We thus extract the real distance and the approximate real time speed of each road user. The results demonstrate that the proposed region proposal generator method for Fast R-CNN outperforms the other methods considered in terms of both accuracy and computation time.