Evaluation of Cnn Architectures for Gait Recognition Based on Optical Flow Maps

This work targets people identification in video based on the way they walk (i.e. gait) by using deep learning architectures. We explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). The low number of training samples for each subject and the use of a test set containing subjects different from the training ones makes the search of a good CNN architecture a challenging task. We carry out a thorough experimental evaluation deploying and analyzing four distinct CNN models with different depth but similar complexity. We show that even the simplest CNN models greatly improve the results using shallow classifiers. All our experiments have been carried out on the challenging TUM-GAID dataset, which contains people in different covariate scenarios (i.e. clothing, shoes, bags).