Spatio-temporal continuous gesture recognition under degraded environments: performance comparison between 3D integral imaging (InIm) and RGB-D sensors.

In this paper, we introduce a deep learning-based spatio-temporal continuous human gesture recognition algorithm under degraded conditions using three-dimensional (3D) integral imaging. The proposed system is shown as an efficient continuous human gesture recognition system for degraded environments such as partial occlusion. In addition, we compare the performance between the 3D integral imaging-based sensing and RGB-D sensing for continuous gesture recognition under degraded environments. Captured 3D data serves as the input to a You Look Only Once (YOLOv2) neural network for hand detection. Then, a temporal segmentation algorithm is employed to segment the individual gestures from a continuous video sequence. Following segmentation, the output is fed to a convolutional neural network-based bidirectional long short-term memory network (CNN-BiLSTM) for gesture classification. Our experimental results suggest that the proposed deep learning-based spatio-temporal continuous human gesture recognition provides substantial improvement over both RGB-D sensing and conventional 2D imaging system. To the best of our knowledge, this is the first report of 3D integral imaging-based continuous human gesture recognition with deep learning and the first comparison between 3D integral imaging and RGB-D sensors for this task.