Human gesture recognition under degraded environments using 3D-integral imaging and deep learning

In this paper, we propose a spatio-temporal human gesture recognition algorithm under degraded conditions using three-dimensional integral imaging and deep learning. The proposed algorithm leverages the advantages of integral imaging with deep learning to provide an efficient human gesture recognition system under degraded environments such as occlusion and low illumination conditions. The 3D data captured using integral imaging serves as the input to a convolutional neural network (CNN). The spatial features extracted by the convolutional and pooling layers of the neural network are fed into a bi-directional long short-term memory (BiLSTM) network. The BiLSTM network is designed to capture the temporal variation in the input data. We have compared the proposed approach with conventional 2D imaging and with the previously reported approaches using spatio-temporal interest points with support vector machines (STIP-SVMs) and distortion invariant non-linear correlation-based filters. Our experimental results suggest that the proposed approach is promising, especially in degraded environments. Using the proposed approach, we find a substantial improvement over previously published methods and find 3D integral imaging to provide superior performance over the conventional 2D imaging system. To the best of our knowledge, this is the first report that examines deep learning algorithms based on 3D integral imaging for human activity recognition in degraded environments.