Adaptive Long Term Motion Pattern Analysis for Moving Object Detection Using Aerial Images

A Saif, Anton Satria Prabuwono, Zainal Rasyid Mahayuddin, Hendri Himawan T.

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Object detection is a fundamental step for automated video analysis in many high level UAV surveillance tasks, including cooperative UAV path planning, navigation control, and automated information analysis. Moving object detection in aerial video is still a challenging problem for the reason that when capturing the video the camera (or the platform) is moving all the time. As a result, the problem is detecting moving object from moving background which is much more difficult than the case that the background is constant. Moving object detection in stationary scene usually modelling the pixel value changes over time, but in aerial video the change does not have regular patterns. Therefore, we model the motion of the background rather than modelling the background directly. In this paper we present a low complexity long term motion analysis based moving object detection approach by using the ideas of robust analysis and spatiotemporal clustering. Here we present an efficient algorithm to estimate the global vehicle-camera motion. Our experimental results show the efficiency of the proposed algorithm.
Original languageEnglish
Pages (from-to)42-51
JournalInternational Journal of Information Systems and Engineering
Volume1
Issue number1
Publication statusPublished - 2013
MoE publication typeA1 Journal article-refereed

Keywords

  • Computer Vision
  • Image Processing
  • Unmanned Aerial Vehicle (UAV)

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