  Acquiring spatio-temporal states of an action is the most crucial step for
action classification. In this paper, we propose a data level fusion strategy,
Motion Fused Frames (MFFs), designed to fuse motion information into static
images as better representatives of spatio-temporal states of an action. MFFs
can be used as input to any deep learning architecture with very little
modification on the network. We evaluate MFFs on hand gesture recognition tasks
using three video datasets - Jester, ChaLearn LAP IsoGD and NVIDIA Dynamic Hand
Gesture Datasets - which require capturing long-term temporal relations of hand
movements. Our approach obtains very competitive performance on Jester and
ChaLearn benchmarks with the classification accuracies of 96.28% and 57.4%,
respectively, while achieving state-of-the-art performance with 84.7% accuracy
on NVIDIA benchmark.
