AFOM: Advanced Flow of Motion Detection Algorithm for Dynamic Camera Videos
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Technological University of the Shannon; National University of Ireland; University of Keele; University of ChesterPublication Date
2022-07-19
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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.Citation
Aribilola, I., Asghar, M. N., Kanwal, N., Ansari, M. S., & Lee, B. (2022, 9-10 June 2022). AFOM: Advanced Flow of Motion detection algorithm for dynamic camera videos. 33rd Irish Signals and Systems Conference (ISSC). Cork, Ireland.Publisher
IEEEAdditional Links
https://ieeexplore.ieee.org/document/9826141Type
Conference ProceedingDescription
©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.ae974a485f413a2113503eed53cd6c53
10.1109/ISSC55427.2022.9826141
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