MsEDNet: multi-scale deep saliency learning for moving object detection

Prashant Patil, Subrahmanyam Murala, Abhinav Dhall, Sachin Chaudhary

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

7 Citations (Scopus)

Abstract

Moving object detection (foreground and background) is an important problem in computer vision. Most of the works in this problem are based on background subtraction. However, these approaches are not able to handle scenarios with infrequent motion of object, illumination changes, shadow, camouflage etc. To overcome these, here a two stage robust and compact method for moving object detection (MOD) is proposed. In first stage, to generate the saliency map, background image is estimated using a temporal histogram technique with the help of several input frames. In the second stage, multiscale encoder-decoder network is used to learn multiscale semantic feature of estimated saliency for foreground extraction. The encoder is used to extract multi-scale features from multi-scale saliency map. The decoder part is designed to learn the mapping of low resolution multi-scale features into high resolution output frame. To observe the efficacy of proposed MsEDNet, experiments are conducted on two benchmark datasets (change detection (CDnet-2014) [1] and Wallflower [2]) for MOD. The precision, recall and F-measure are used as performance parameter for comparison with the existing state-of-the-art methods. Experimental results show a significant improvement in detection accuracy and decrement in execution time as compared to the state-of-the-art methods for MOD.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
EditorsTadahiko Murata
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1670-1675
Number of pages6
ISBN (Electronic)9781538666500
ISBN (Print)9781538666517
DOIs
Publication statusPublished - 2018
EventIEEE International Conference on Systems, Man and Cybernetics 2018 - Miyazaki, Japan
Duration: 7 Oct 201810 Oct 2018

Conference

ConferenceIEEE International Conference on Systems, Man and Cybernetics 2018
Abbreviated titleSMC 2018
CountryJapan
CityMiyazaki
Period7/10/1810/10/18

Keywords

  • Background estimation
  • CNN
  • Encoder-Decoder network
  • foreground detection
  • Histogram

Cite this

Patil, P., Murala, S., Dhall, A., & Chaudhary, S. (2018). MsEDNet: multi-scale deep saliency learning for moving object detection. In T. Murata (Ed.), Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 1670-1675). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SMC.2018.00289