Published on: April 2026
VIDEO ACTION RECOGNITION IN NOISY ENVIRONMENTS
Rohini Sharanya P Nagashashank Panyam Ujwala Sai Priya R Lokesh Goud
DR. B. Shankar Nayak
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Abstract
Video action recognition in real-world environments presents many challenges due to noise, motion blur, dynamic backgrounds, and occlusion in video frames. In this paper, we propose a robust system for recognizing human actions using video data with both traditional machine learning and deep learning techniques. The proposed system employs two different preprocessing methods (Optical Flow and Background Subtraction) and two different recognition methods (Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM) and 3D Convolutional Neural Networks (3D-CNN)) as advanced techniques to identify actions [1].
How to Cite this Paper
P, R. S., Panyam, N., Priya, U. S. & Goud, R. L. (2026). Video Action Recognition in Noisy Environments. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.368
P, Rohini, et al.. "Video Action Recognition in Noisy Environments." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.368.
P, Rohini,Nagashashank Panyam,Ujwala Priya, and R Goud. "Video Action Recognition in Noisy Environments." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.368.
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References
[1] In this paper, the authors discuss various methods of recognizing human actions in video sequences. They provide an overview of existing work up to early developments, with an emphasis on computer vision, motion analysis, and video processing systems.[2] Ahmad et al. survey the state-of-the-art video action recognition methods and provide recommendations for future research directions. They focus on recent studies, including those related to deep learning and spatiotemporal feature extraction techniques.
[3] Behara & Raghunadh present a real-time action recognition system that can be used for activity monitoring and surveillance, which can also be applied in workplace safety and behavioral analysis.
[4] Suneetha discusses various approaches to video-based action recognition, providing a general overview of techniques for handling motion patterns and temporal information in video data.
[5] Darmono & Muhiqqin conducted a comparative study between traditional motion detection methods such as Optical Flow and feature-based techniques like Histogram of Oriented Gradients (HOG) for action recognition using real-world video datasets.
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- •Published on: Apr 14 2026
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