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-theart performance with 84.7% accuracy on NVIDIA benchmark.
On the other hand, REF proposes a data level fusion strategy, Motion Fused Frames (MFFs), where different modalities can be fused with very little modification to the network and computational cost.
Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Computer Science