Dynamic Gesture Recognition Based on the Multi-modality Fusion Temporal Segment Networks

被引：0
|
作者：
Zheng, Mingyao [1 ]
Tie, Yun [1 ]
Qi, Lin [1 ]
Jiang, Shengnan [1 ]
机构：
[1] Zhengzhou Univ, Sch Informat Engn, Zhengzhou, Peoples R China
来源：
2019 8TH INTERNATIONAL SYMPOSIUM ON NEXT GENERATION ELECTRONICS (ISNE) | 2019年
关键词：
Dynamic gesture recognition; Multi-modality fusion; Temporal segment networks; Optical flow;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Gesture recognition is applied in various intelligent scenes. In this paper, we propose the multi-modality fusion temporal segment networks (MMFTSN) model to solve dynamic gestures recognition. Three gesture modalities: RGB, Depth and Optical flow (OF) video data are equally segmented and randomly sampled. Then, the sampling frames are classified using convolutional neural network. Finally, fusing three kinds of modality classification results. MMFTSN is used to obtain the recognition accuracy of 60.2% on the gesture database Chalearn LAP IsoGD, which is better than the result of related algorithms. The results show that the improved performance of our MMFTSN model.
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页数：3
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