Cross Training for Pedestrian recognition using Convolutional Neural networks

International audienceIn 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

Detection of Pedestrians at Far distance.

International audiencePedestrian detection is a well-studied problem. Even though many datasets contain challenging case studies, the performances of new methods are often only reported on cases of reasonable difficulty. In particular, the issue of small scale pedestrian detection is seldom considered. In this paper, we focus on the detection of small scale pedestrians, i.e., those that are at far distance from the camera. We show that classical features used for pedestrian detection are not well suited for our case of study. Instead, we propose a convolutional neural network based method to learn the features with an end-to- end approach. Experiments on the Caltech Pedestrian Detection Benchmark showed that we outperformed existing methods by more than 10% in terms of log-average miss rate