RICA-MD: a refined ICA algorithm for motion detection

Chao Zhang, Xiaopei Wu, Jianchao Lu, Xi Zheng*, Alireza Jolfaei, Quan Z. Sheng, Dongjin Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

With the rapid development of various computing technologies, the constraints of data processing capabilities gradually disappeared, and more data can be simultaneously processed to obtain better performance compared to conventional methods. As a standard statistical analysis method that has been widely used in many fields, Independent Component Analysis (ICA) provides a new way for motion detection by extracting the foreground without precisely modeling the background. However, most existing ICA-based motion detection algorithms use only two-channel data for source separation and simply generate the observation vectors by decomposing and reconstructing the images by row, hence they cannot obtain an integrated and accurate shape of the moving objects in complex scenes. In this article, we propose a refined ICA algorithm for motion detection (RICA-MD), which fuses a larger number of channels than conventional ICA-based motion detection algorithms to provide more effective information for foreground extraction. Meanwhile, we propose four novel methods for generating observation vectors to further cover the diverse motion styles of the moving objects. These improvements enable RICA-MD to effectively deal with slowly moving objects, which are difficult to detect using conventional methods. Our quantitative evaluation in multiple scenes shows that our proposed method is able to achieve a better performance at an acceptable cost of false alarms.
Original languageEnglish
Article number17
Pages (from-to)1-17
Number of pages17
JournalACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)
Volume17
Issue number1s
DOIs
Publication statusPublished - Mar 2021

Keywords

  • Motion detection
  • Independent Component Analysis (ICA)
  • multi-channel input
  • observation signal reconstruction

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