Aribilola, I, Asghar, MN, Kanwal, N, Ansari, MS, Lee, B and Xplore, IEEE (2022) AFOM: ADVANCED FLOW OF MOTION DETECTION ALGORITHM FOR DYNAMIC CAMERA VIDEOS. 2022 33rd Irish Signals and Systems Conference (ISSC). ISSN 2688-1446

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The surveillance videos taken from dynamic cam-eras are susceptible to multiple security threats like replay attacks, man-in-the-middle attacks, pixel correlation attacks etc. Using unsupervised learning, it is a challenge to detect objects in such surveillance videos, as fixed objects may appear to be in motion alongside the actual moving objects. But despite this challenge, the unsupervised learning techniques are efficient as they save object labelling and model training time, which is usually a case with supervised learning models. This paper proposes an effective computer vision-based object identification algorithm that can detect and separate stationary objects from moving objects in such videos. The proposed Advanced Flow Of Motion (AFOM) algorithm takes advantage of motion estimation between two consecutive frames and induces the estimated motion back to the frame to provide an improved detection on the dynamic camera videos. The comparative analysis demonstrates that the proposed AFOM outperforms a traditional dense optical flow (DOF) algorithm with an average increased difference of 56 % in accuracy, 61 % in precision, and 73 % in pixel space ratio (PSR), and with minimal higher object detection timing.

Item Type: Article
Additional Information: The final version of this conference article and all relevant information related to it, including copyrights, can be found on the publisher website.
Subjects: T Technology > T Technology (General)
Related URLs:
Depositing User: Symplectic
Date Deposited: 07 Oct 2022 13:42
Last Modified: 07 Oct 2022 13:42

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