Arnab Halder
A Locally Weighted Linear Regression Based Approach for Arbitrary Moving Shaky and Non-Shaky Video Classification
Halder, Arnab; Shivakumara, Palaiahnakote; Pal, Umapada; Blumenstein, Michael; Ghosal, Palash
Authors
Dr Shivakumara Palaiahnakote S.Palaiahnakote@salford.ac.uk
Lecturer
Umapada Pal
Michael Blumenstein
Palash Ghosal
Abstract
Classification and identification of objects are complex and challenging in pattern recognition and artificial intelligence if a shaky and nonshaky camera captures the videos at different distances during the day and nighttime. This work presents a model for classifying a given video as a static, uniform, or arbitrarily moving videos so that the complexity of the problem can be reduced. To avoid the threat of different distances between the objects and the camera, the proposed work introduces new steps for estimating the depth of the objects in the video frames. We explore locally weighted linear regression for feature extraction from depth information based on the notion that the regression line fits almost all the points for uniformity and does not fit for arbitrary moving. The extracted features are fed to a random forest classifier to classify static, uniform, or arbitrary moving video. The results on a large dataset, which includes videos captured day and night, show that the proposed method successfully classifies static, uniform and arbitrary videos with 0.86, 1.00 and 0.67 F-measures, respectively. Overall, our method obtains 87% accuracy for classification of static, uniform and arbitrary video, which is superior to the state-of-the-art methods.
Citation
Halder, A., Shivakumara, P., Pal, U., Blumenstein, M., & Ghosal, P. (2023). A Locally Weighted Linear Regression Based Approach for Arbitrary Moving Shaky and Non-Shaky Video Classification. International Journal of Pattern Recognition and Artificial Intelligence, 38(1), https://doi.org/10.1142/S0218001423510199
Journal Article Type | Article |
---|---|
Acceptance Date | Nov 19, 2023 |
Publication Date | Dec 8, 2023 |
Deposit Date | Nov 15, 2024 |
Publicly Available Date | Dec 9, 2024 |
Journal | International Journal of Pattern Recognition and Artificial Intelligence |
Print ISSN | 0218-0014 |
Electronic ISSN | 1793-6381 |
Publisher | World Scientific Publishing |
Peer Reviewed | Peer Reviewed |
Volume | 38 |
Issue | 1 |
DOI | https://doi.org/10.1142/S0218001423510199 |
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Copyright Statement
Electronic version of an article published as International Journal of Pattern Recognition and Artificial Intelligence 2024 38:01 https://doi.org/10.1142/S0218001423510199 © copyright World Scientific Publishing Company https://www.worldscientific.com/worldscinet/ijprai
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