An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition
Paper
Paper/Presentation Title | An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition |
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Presentation Type | Paper |
Authors | Wu, Di, Sharma, Nabin and Blumenstein, Michael |
Journal or Proceedings Title | Proceedings of 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ) |
Number of Pages | 6 |
Year | 2019 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
Digital Object Identifier (DOI) | https://doi.org/10.1109/IVCNZ.2018.8634660 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/abstract/document/8634660 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/8625178/proceeding |
Conference/Event | 2018 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. |
Keywords | Action Recognition; Convolutional Neural Networks; Deep Learning; Computer Vision |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4602. 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 Affiliations | University of Technology Sydney |
https://research.usq.edu.au/item/z4y24/an-end-to-end-hierarchical-classification-approach-for-similar-gesture-recognition
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