Section: New Results
Recognizing Pedestrians using Cross-Modal Convolutional Networks
Participants : Danut-Ovidiu Pop, Fawzi Nashashibi.
This year, we have continued our research, which is based on multi-modal image fusion schemes with deep learning classification methods. We propose four different learning patterns based on Cross-Modality deep learning of Convolutional Neural Networks:
(1) a Particular Cross-Modality Learning;
(2) a Separate Cross-Modality Learning;
(3) a Correlated Cross-Modality Learning and
(4) an Incremental CrossModality Learning model.
Moreover, we also design a new variation of a Lenet architecture, which improves the classification performance. Finally, we propose to learn this model with the incremental cross-modality approach using optimal learning settings, obtained with a K-fold Cross Validation pattern. This method outperforms the state-of-the-art classifier provided with Daimler datasets on both non-occluded and partially-occluded pedestrian tasks.