Learning Spatiotemporal Features using 3DCNN and Convolutional LSTM for Gesture Recognition

Liang Zhang, G Zhu, P. Shen, Juan Song, Syed Shah, Mohammed Bennamoun

Research output: Chapter in Book/Conference paperConference paper

53 Citations (Scopus)

Abstract

Gesture recognition aims at understanding the ongoing human gestures. In this paper, we present a deep architecture to learn spatiotemporal features for gesture recognition. The deep architecture first learns 2D spatiotemporal feature maps using 3D convolutional neural networks (3DCNN) and bidirectional convolutional long-short-term-memory networks (ConvLSTM). The learnt 2D feature maps can encode the global temporal information and local spatial information simultaneously. Then, 2DCNN is utilized further to learn the higher-level spatiotemporal features from the 2D feature maps for the final gesture recognition. The spatiotemporal correlation information is kept through the whole process of feature learning. This makes the deep architecture an effective spatiotemporal feature learner. Experiments on the ChaLearn LAP large-scale isolated gesture dataset (IsoGD) and the Sheffield Kinect Gesture (SKIG) dataset demonstrate the superiority of the proposed deep architecture.
Original languageEnglish
Title of host publicationLearning Spatiotemporal Features using 3DCNN and Convolutional LSTM for Gesture Recognition
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages3120-3128
ISBN (Print)9781538610343
DOIs
Publication statusPublished - 1 Aug 2017
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: 22 Oct 201729 Oct 2017

Conference

Conference16th IEEE International Conference on Computer Vision, ICCV 2017
CountryItaly
CityVenice
Period22/10/1729/10/17

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