Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network

Gesture recognition is one of the important ways of human-computer interaction, which is mainly detected by visual technology. The temporal and spatial features are extracted by convolution of the video containing gesture. However, compared with the convolution calculation of a single image, multiframe image of dynamic gestures has more computation, more complex feature extraction, and more network parameters, which affects the recognition efficiency and real-time performance of the model. To solve above problems, a dynamic gesture recognition model based on CBAM-C3D is proposed. Key frame extraction technology, multimodal joint training, and network optimization with BN layer are used for making the network performance better. The experiments show that the recognition accuracy of the proposed 3D convolutional neural network combined with attention mechanism reaches 72.4% on EgoGesture dataset, which is improved greatly compared with the current main dynamic gesture recognition methods, and the effectiveness of the proposed algorithm is verified.

Download Full-text

Gesture Recognition Algorithm Based on New EMG Representation and Convolutional Neural Network

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9326749 ◽

2020 ◽

Author(s):

Rui Gao ◽

Jian Guo ◽

Yupeng He ◽

Shulong Dong ◽

Peiyu Liu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Gesture Recognition ◽

Recognition Algorithm

Download Full-text

Dynamic Gesture Recognition Method Based on Convolutional Neural Network

2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) ◽

10.1109/ithings/greencom/cpscom/smartdata.2019.00085 ◽

2019 ◽

Author(s):

Xiaoyu Xu ◽

Qingmin Meng ◽

Lizhen Deng

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Gesture Recognition ◽

Recognition Method ◽

Dynamic Gesture Recognition

Download Full-text

Detection and Classification of Early Decay on Blueberry Based on Improved Deep Residual 3D Convolutional Neural Network in Hyperspectral Images

Scientific Programming ◽

10.1155/2020/8895875 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Shicheng Qiao ◽

Qinghu Wang ◽

Jun Zhang ◽

Zhili Pei

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Performance ◽

Hyperspectral Image ◽

Hyperspectral Images ◽

Training Time ◽

Spatial Features ◽

Fast Training ◽

Novel Strategy ◽

3D Cnn

Recently, the automatic detection of decayed blueberries is still a challenge in food industry. Early decay of blueberries happens on surface peel, which may adopt the feasibility of hyperspectral imaging mode to detect decayed region of blueberries. An improved deep residual 3D convolutional neural network (3D-CNN) framework is proposed for hyperspectral images classification so as to realize fast training, classification, and parameter optimization. Rich spectral and spatial features can be rapidly extracted from samples of complete hyperspectral images using our proposed network. This combines the tree structured Parzen estimator (TPE) adaptively and selects the super parameters to optimize the network performance. In addition, aiming at the problem of few samples, this paper proposes a novel strategy to enhance the hyperspectral image sample data, which can improve the training effect. Experimental results on the standard hyperspectral blueberry datasets show that the proposed framework improves the classification accuracy compared with AlexNet and GoogleNet. In addition, our proposed network reduces the number of parameters by half and the training time by about 10%.

Download Full-text