Cross Training for Pedestrian recognition using Convolutional Neural networks

In recent years, deep learning classification methods, specially Convolutional Neural Networks (CNNs), combined with multi-modality image fusion schemes have achieved remarkable performance. Hence, in this paper, we focus on improving the late-fusion scheme for pedestrian classification on the Daimler stereo vision data set. We propose cross training method in which a CNN for each independent modality (Intensity, Depth, Flow) is trained and validated on different modalities, in contrast to classical training method in which the training and validation of each CNN is on same modality. The CNN outputs are then fused by a Multi-layer Perceptron (MLP) before making the recognition decision.