Skip to main content

Research Repository

Advanced Search

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

A Locally Weighted Linear Regression Based Approach for Arbitrary Moving Shaky and Non-Shaky Video Classification Thumbnail


Authors

Arnab Halder

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

Files





You might also like



Downloadable Citations