Moving Object Detection Using Semantic Convolutional Features

Zainal Rasyid Mahayuddin*, A Saif*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

39 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)24-41
Number of pages18
JournalJournal of Information System and Technology Management
Volume7
Issue number29
DOIs
Publication statusPublished - 31 Dec 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Computer Vision (CV)
  • Deep Learning
  • Image Processing

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