Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks

Authors

Pichao Wang, University of WollongongFollow
Wanqing Li, University of WollongongFollow
Song Liu, University of WollongongFollow
Yuyao Zhang, University of WollongongFollow
Zhimin Gao, University of WollongongFollow
Philip O. Ogunbona, University of WollongongFollow

RIS ID

114192

Publication Details

Wang, P., Li, W., Liu, S., Zhang, Y., Gao, Z. & Ogunbona, P. (2016). Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks. Proceedings - 23rd International Conference on Pattern Recognition (ICPR) (pp. 13-18). United States: IEEE.

Abstract

This paper addresses the problem of continuous gesture recognition from sequences of depth maps using Convolutional Neural networks (ConvNets). The proposed method first segments individual gestures from a depth sequence based on quantity of movement (QOM). For each segmented gesture, an Improved Depth Motion Map (IDMM), which converts the depth sequence into one image, is constructed and fed to a ConvNet for recognition. The IDMM effectively encodes both spatial and temporal information and allows the fine-tuning with existing ConvNet models for classification without introducing millions of parameters to learn. The proposed method is evaluated on the Large-scale Continuous Gesture Recognition of the ChaLearn Looking at People (LAP) challenge 2016. It achieved the performance of 0.2655 (Mean Jaccard Index) and ranked 3rd place in this challenge.