  We present a new deep learning approach for real-time 3D human action
recognition from skeletal data and apply it to develop a vision-based
intelligent surveillance system. Given a skeleton sequence, we propose to
encode skeleton poses and their motions into a single RGB image. An Adaptive
Histogram Equalization (AHE) algorithm is then applied on the color images to
enhance their local patterns and generate more discriminative features. For
learning and classification tasks, we design Deep Neural Networks based on the
Densely Connected Convolutional Architecture (DenseNet) to extract features
from enhanced-color images and classify them into classes. Experimental results
on two challenging datasets show that the proposed method reaches
state-of-the-art accuracy, whilst requiring low computational time for training
and inference. This paper also introduces CEMEST, a new RGB-D dataset depicting
passenger behaviors in public transport. It consists of 203 untrimmed
real-world surveillance videos of realistic normal and anomalous events. We
achieve promising results on real conditions of this dataset with the support
of data augmentation and transfer learning techniques. This enables the
construction of real-world applications based on deep learning for enhancing
monitoring and security in public transport.
