A Hybrid Network for Large-Scale Action Recognition from RGB and Depth Modalities

Sensors
Huogen WangPichao Wang

Abstract

The paper presents a novel hybrid network for large-scale action recognition from multiple modalities. The network is built upon the proposed weighted dynamic images. It effectively leverages the strengths of the emerging Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches to specifically address the challenges that occur in large-scale action recognition and are not fully dealt with by the state-of-the-art methods. Specifically, the proposed hybrid network consists of a CNN based component and an RNN based component. Features extracted by the two components are fused through canonical correlation analysis and then fed to a linear Support Vector Machine (SVM) for classification. The proposed network achieved state-of-the-art results on the ChaLearn LAP IsoGD, NTU RGB+D and Multi-modal & Multi-view & Interactive ( M 2 I ) datasets and outperformed existing methods by a large margin (over 10 percentage points in some cases).

References

Nov 2, 2004·Neural Computation·David R HardoonJohn Shawe-Taylor
Mar 7, 2012·IEEE Transactions on Pattern Analysis and Machine Intelligence·Shuiwang JiKai Yu
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Kaiming HeJian Sun
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ming-Ming ChengShi-Min Hu
Nov 22, 2015·Bioinformatics·Kévin VervierJean-Philippe Vert
Jun 14, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Shaoqing RenJian Sun
Mar 10, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Basura FernandoTinne Tuytelaars
Apr 10, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Amir ShahroudyGang Wang

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Software Mentioned

SI
Spatial Pyramid Pooling ( SPP )
MotionNet
ImageNet
Clips
+ CNN
Tensorflow
View invariant
SSSCA
ConvLSTM

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