  Nowadays, the interaction between humans and robots is constantly expanding,
requiring more and more human motion recognition applications to operate in
real time. However, most works on temporal action detection and recognition
perform these tasks in offline manner, i.e. temporally segmented videos are
classified as a whole. In this paper, based on the recently proposed framework
of Temporal Recurrent Networks, we explore how temporal context and human
movement dynamics can be effectively employed for online action detection. Our
approach uses various state-of-the-art architectures and appropriately combines
the extracted features in order to improve action detection. We evaluate our
method on a challenging but widely used dataset for temporal action
localization, THUMOS'14. Our experiments show significant improvement over the
baseline method, achieving state-of-the art results on THUMOS'14.
