Fusion of Bag of Visual Words with Neural Network for Human Action Recognition

Recognition of human actions is one of the important tasks in various applications. It finds a number of applications in a range of areas. Some of the challenges confronted are noise that peeps in and shape of the action being performed by the subject. In this paper, a bag-of-visual-words is extract...

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Bibliographic Details
Published in2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) pp. 14 - 19
Main Authors Anju Latha Nair, S., Megalingam, Rajesh Kannan
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.01.2022
Subjects
Bag of visual words
Filtering
Human Action Recognition
Learning Vector Quantization
Neural network
Neural networks
Optical filters
Optical flow
Shape
Transforms
Vector quantization
Visualization
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Summary:Recognition of human actions is one of the important tasks in various applications. It finds a number of applications in a range of areas. Some of the challenges confronted are noise that peeps in and shape of the action being performed by the subject. In this paper, a bag-of-visual-words is extracted with the help of Scale Invariant Feature Transform (SIFT) and optical flow. It represents spatial as well as time-dependent feature points. Sobel edge filter and median filtering is used to minimize shadow effect and suppress background noise respectively. The classifier used is multiclass Learning-vector Quantisation-based. The well-known KTH dataset is used as benchmark.
DOI:10.1109/Confluence52989.2022.9734221