CNN-based Moving Vehicle Recognition using GMM-based Foreground Modeling, Level-set based Segmentation and Kalman Filter-based Tracking

This research work introduces a moving vehicle detection, classification and tracking framework based on a successful combination of machine and deep learning models. A vehicle detection solution combining a Gaussian Mixture Model (GMM)-based video foreground extraction to a level-set based frame segmentation is proposed first. A vehicle counting process is then performed on the obtained detections applying a Kalman filtering model. Next, the detected vehicles are recognized by their type applying a convolutional neural network (CNN) - based classifier and also by using the motion-based tracking results. Finally, the performed simulations are discussed here.