Deep learning approaches, platforms, datasets for behaviorbased recognition: a survey

Abstract

Video surveillance is an extensively used tool due to the high rate of atypical behavior and many cameras that enable video capture and storage. Unfortunately, most of these cameras are operator dependent for stored content analysis. This limitation necessitates the provision of an automatic behavior identification system. This behavior identification can be achieved using unsupervised (generative) computer vision methods. Deep learning makes it possible to model human behavior regardless of where they could be. We attempt to classify current research work to report the ongoing trends in human behavior recognition using deep learning algorithms. This paper reviews various aspects, like the ones associated with machine learning and deep learning models, human activity recognition (HAR), deep learning frameworks/tools, abnormal behavior datasets, and a variety of other current trends in the field of automatic learning. All these are to give the researcher a sense of direction in this area.