Pedestrian Recognition through Different Cross-Modality Deep Learning Methods

In this work, we propose a multi-task model which explores deep learning, and more specifically recurrent neural networks to process AIS data stream for multiple purposes:

We study the inductive bias in such a learning regime by analyzing smoothness, stability and approximation properties for this kernel, and comparing it to other deep kernels

At the selection stage of each active learning cycle, we mea- sure the relevance of labelling a particular image pair by rank- ing the aforementioned cross-modality prediction

We pro- pose cross training method in which a CNN for each in- dependent modality (Intensity, Depth, Flow) is trained and validated on different modalities, in contrast to

• Train all pedestrian samples also using the pedestrian actions tags (cross/not cross) with the CNN as mentioned above for detection and action recognition based on the Joint

In this paper, we systematically depicted different cross- modality learning approaches of four methods based on Con- volutional Neural Networks for pedestrian recognition: (1)

We propose different methods based on Cross-Modality deep learning of CNNs: (1) a correlated model where a unique CNN is trained with Intensity, Depth and Flow images for each

Our best single model was a convolutional neural network trained to predict emotions from static frames using two large data sets, the Toronto Face Database and our own set of