Analytics of deep neural network in change detection

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Recently, deep neural networks (DNNs) have demonstrated excellent performance for change detection. The DNN-based background subtraction automatically discovers background features from datasets and outperforms traditional background modeling based on handcraft features and/or subtraction strategies. Most researchers mainly discuss the accuracy of foreground detection and do not analyze how and why the DNN works well for change detection tasks. It is necessary to understand what the DNN learns as background features in order to discuss the potential of the DNN in background subtraction. In this paper, we focus on the filters in the first convolution layer and the activations of neurons in the last fully connected layer to understand the behavior of the DNN. From the experiment, we found that 1) the first layer performs the role of background subtraction using several filters, and 2) the last layer categorizes some background changes into a group without supervised signals. These findings suggest the possibility of a new background modeling strategy based on data-driven extracted features.

Original languageEnglish
Title of host publication2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538629390
DOIs
Publication statusPublished - Oct 20 2017
Event14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017 - Lecce, Italy
Duration: Aug 29 2017Sep 1 2017

Publication series

Name2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017

Other

Other14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017
CountryItaly
CityLecce
Period8/29/179/1/17

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Minematsu, T., Shimada, A., & Taniguchi, R. I. (2017). Analytics of deep neural network in change detection. In 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017 [8078550] (2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AVSS.2017.8078550