Change detection in multitemporal monitoring images under low illumination

Yong Zhu, Zhenhong Jia, Jie Yang, Nik Kasabov

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
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Video surveillance may involve the simultaneous monitoring of a large number of areas. Real-time automatic change detection of a monitoring area (such as involving the movement of people or vehicles) can reduce risks incurred in negligent manual observation. However, the low signal-to-noise ratio (SNR) of dark environments can significantly corrupt camera images, making it difficult for machine learning surveillance systems to detect small changes in monitored images. In addition, in the absence of changes between two multitemporal monitoring images, sensor noise can lead to false alarms. The objective of this paper is to reduce the effect of sensor noise on change detection of monitored images and the run time of change detection algorithms. For these purposes, we proposed a novel multitemporal monitoring image change detection algorithm based on morphological structure filtering and normalized fusion difference image. First, the random noise in two surveillance images was removed using a multidirectional weighted multiscale series of a morphological filter. Next, two difference images were obtained by using the compression log-ratio operator and the mean ratio operator, and a fusion difference image was obtained by equal-weight fusion of the two difference images. Then, the sigmoid function was used to compress the fusion difference map to obtain a normalized fusion difference image, and a median filter was used to obtain a final difference image. Finally, the k-means clustering algorithm was utilized to obtain the change detection results. The experimental results demonstrate that the proposed method can accurately detect changes in a night monitoring scene in real time. Subjective and objective evaluation of the experimental results demonstrate that the proposed method is superior to reference algorithms in terms of change detection accuracy, time and robustness.

Original languageEnglish
Article number9137696
Pages (from-to)126700-126712
Number of pages13
JournalIEEE Access
Early online date9 Jul 2020
Publication statusPublished online - 9 Jul 2020

Bibliographical note

Funding Information:
This work was supported in part by the National Science Foundation of China under Grant U1803261, and in part by the International Science and Technology Cooperation Project of the Ministry of Education of China under Grant DICE 2016–2196.

Publisher Copyright:
© 2013 IEEE.

Copyright 2020 Elsevier B.V., All rights reserved.


  • Change detection
  • low illumination monitoring image
  • morphological structure filtering
  • normalized fusion difference map


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