An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition

Paper

Wu, Di, Sharma, Nabin and Blumenstein, Michael. 2019. "An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition." 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). Auckland, New Zealand 19 - 21 Nov 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IVCNZ.2018.8634660
Paper/Presentation Title

An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition

Presentation TypePaper
AuthorsWu, Di, Sharma, Nabin and Blumenstein, Michael
Journal or Proceedings TitleProceedings of 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)
Number of Pages6
Year2019
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
Digital Object Identifier (DOI)https://doi.org/10.1109/IVCNZ.2018.8634660
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/abstract/document/8634660
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/8625178/proceeding
Conference/Event2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)
Event Details
2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)
Parent
Image and Vision Computing New Zealand (IVCNZ)
Delivery
In person
Event Date
19 to end of 21 Nov 2018
Event Location
Auckland, New Zealand
Abstract

Human action recognition from the RGB video is widely applied on varies real applications. Many works have been done by researchers in computer vision and machine learning area to address the challenges and complexity involved in video-based human action recognition. Deep learning approaches including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been introduced in the human action recognition research area. However, due to the drawbacks of the CNNs, recognizing actions with similar gestures and describing complex actions is still very challenging. Hence, an end-to-end hierarchical classification architecture has been proposed in this paper to resolve the confusion between similar gesture. The proposed approach firstly classifies the whole dataset and generates the accuracy for each class in stage 1. Based on the confusion matrix obtained from stage-1, the approach combines the most confused similar gesture pairs into one class, and classify them along with all other class, in the stage-2. In stage 3, similar gesture pairs will be classified by binary classifiers, which will increase the performance of each class and the overall accuracy. We apply and evaluate the developed models to recognize the similar human actions on the both KTH and UCF101 dataset. The result shows that the proposed approach can boost the classification performance on both the datasets. The proposed architecture is robust and any classification technique can be used in stage 1 and stage 2.

KeywordsAction Recognition; Convolutional Neural Networks; Deep Learning; Computer Vision
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
460304. Computer vision
Public Notes

The accessible file is the accepted version of the paper. Please refer to the URL for the published version.

Byline AffiliationsUniversity of Technology Sydney
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