RETRACTED: Learning spatiotemporal features with 3D DenseNet and attention for gesture recognition

Abstract

Gesture recognition aims at understanding dynamic gestures of the human body and is one of the most important ways of human–computer interaction; to extract more effective spatiotemporal features in gesture videos for more accurate gesture classification, a novel feature extractor network, spatiotemporal attention 3D DenseNet is proposed in this study. We extend DenseNet with 3D kernels and Refined Temporal Transition Layer based on Temporal Transition Layer, and we also explore attention mechanism in 3D ConvNets. We embed the Refined Temporal Transition Layer and attention mechanism in DenseNet3D, named the proposed network “spatiotemporal attention 3D DenseNet.” Our experiments show that our Refined Temporal Transition Layer performs better than Temporal Transition Layer and the proposed spatiotemporal attention 3D DenseNet in each modality outperforms the current state-of-the-art methods on the ChaLearn LAP Large-Scale Isolated gesture dataset. The code and pretrained model are released in https://github.com/dzf19927/STA3D .

RETRACTED: Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

“…With the emergence and improvement of the depth sensor-Microsoft Kinect, 5 the method of using 3D structure provided by 3D sensor to study human behavior recognition has become a hot research field. 6 In recent years, deep learning based approach has achieved outstanding results in image recognition and classification. Most of these methods learn motion features from RGB-D image sequences.…”