Journal
IEEE TRANSACTIONS ON MULTIMEDIA
Volume 21, Issue 4, Pages 1011-1021Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2018.2869278
Keywords
Continuous gesture recognition; 3DCNN; convolutional LSTM; dilation
Categories
Funding
- National Natural Science Foundation of China [61702390]
- Fundamental Research Funds for the Central Universities [JB181001]
- Key Research and Development Program of Shaanxi Province [2018ZDXM-GY-036]
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Continuous gesture recognition aims at recognizing the ongoing gestures from continuous gesture sequences and is more meaningful for the scenarios, where the start and end frames of each gesture instance are generally unknown in practical applications. This paper presents an effective deep architecture for continuous gesture recognition. First, continuous gesture sequences are segmented into isolated gesture instances using the proposed temporal dilated Res3D network. A balanced squared hinge loss function is proposed to deal with the imbalance between boundaries and nonboundaries. Temporal dilation can preserve the temporal information for the dense detection of the boundaries at fine granularity, and the large temporal receptive field makes the segmentation resultsmore reasonable and effective. Then, the recognition network is constructed based on the 3-D convolutional neural network (3DCNN), the convolutional long-short-term-memory network (ConvLSTM), and the 2-D convolutional neural network (2DCNN) for isolated gesture recognition. The 3DCNN-ConvLSTM-2DCNN architecture is more effective to learn long-term and deep spatiotemporal features. The proposed segmentation and recognition networks obtain the Jaccard index of 0.7163 on the Chalearn LAP ConGD dataset, which is 0.106 higher than the winner of 2017 ChaLearn LAP Large-Scale Continuous Gesture Recognition Challenge.
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