Currently, background subtraction is being actively studied in many image processing applications. Nuclear Norm Minimization (NNM) and Weighted Nuclear Norm Minimization (WNNM) are commonly used background subtraction methods based on Robust Principal Component Analysis (RPCA). However, these techniques approximate the RPCA rank function and take the form of an iterative optimization algorithm. Therefore, due to the approximation, the NNM solution can not converge if the number of frames is small. In addition, the NNM and WNNM processing times are delayed because of their iterative optimization schemes. Thus, NNM and WNNM are not suitable for real-time background subtraction. In order to overcome these limitations, this paper presents a real-time background subtraction method using tensor decomposition in accordance with the recent tensor analysis research trend. In this study, we used the closed form TUCKER2 decomposition solution to omit the iterative process while retaining the L1 norm of the RPCA rank function. This proposed method allows for convergence even when the number of frames is small. Compared to NNM and WNNM, the proposed method reduces the processing time by more than 80 times and has a higher precision even when the number of frames are less than 10.
|Title of host publication||2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2019 Mar 4|
|Event||10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States|
Duration: 2018 Nov 12 → 2018 Nov 15
|Name||2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings|
|Conference||10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018|
|Period||18/11/12 → 18/11/15|
Bibliographical noteFunding Information:
This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program(10073229, Development of 4K highresolution image based LSTM network deep learning process pattern recognition algorithm for real-time parts assembling of industrial robot for manufacturing) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea)
© 2018 APSIPA organization.
All Science Journal Classification (ASJC) codes
- Information Systems