Moving Object Detection Using Semantic Convolutional Features
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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Author
Date
2022-12-31
Major/Subject
Mcode
Degree programme
Language
en
Pages
18
24-41
24-41
Series
Journal of Information System and Technology Management, Volume 7, issue 29
Abstract
Moving object detection from aerial images remains an unsolved problem in computer vision research domain. Detection results are not precise due to blurry aerial images, thin edges and noise. Various methods were previously proposed for moving object detection which could not provide robust results due many challenges, i.e., noise, motion detection, lack of appropriate features, lack of effective classification approach, complex background and variations in illumination. This research proposes an efficient method for moving object detection using convolutional semantic features from VGG-16 to use motion patterns to facilitate detection in each frame and provides smaller area as region of interest. Proposed method reduces probability motion intensity information getting lost in case of same coloured object in the background and thus minimizes background complexity. After that, proposed method performs semantic features distance measurement to calculate linear distances in each frame. In this context, if there is any frame loss due to noise or illumination variation, proposed method uses Kalman filter to process that frame by illuminating noise. Finally, decision for final moving detection is determined using random forest classifier from semantic convolutional feature vector by generating a set of probabilities for each class. Experimental results show that the proposed method can detect moving objects efficiently, which in turn will decrease the operating time and increase the detection rate compared to previous research methods.Description
Keywords
Computer Vision (CV), Deep Learning, Image Processing
Other note
Citation
Mahayuddin , Z R & Saif , A 2022 , ' Moving Object Detection Using Semantic Convolutional Features ' , Journal of Information System and Technology Management , vol. 7 , no. 29 , pp. 24-41 . https://doi.org/10.35631/JISTM.729003
