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Multi-Channel Fused Lasso for Motion Detection in Dynamic Video Scenarios

Publiceringsår

2023

Upphovspersoner

Gao Rong; Liu Xin; Yang Jingyu; Yue Huanjing

Abstrakt

Motion detection is a fundamental step in analyzing video sequences, capable of enhancing consumer electronics products with increased intelligence, interactivity, and convenience. Structured and fused sparsity has been used in previous works to normalize the foreground signal due to the foreground’s spatial and temporal coherence. As far as we are aware, no previous works have studied the group prior to multi-channels (such as the RGB) to the foreground signals. However, a multi-channel signal is the correct representation of a pixel. Under the condition that one pixel is equal (similar) to its neighboring pixels, it’s reasonable that the three channels of RGB should also be identical (similar). This work investigates the smoothness of multi-channel signals by proposing a novel regularizer named the Multi-Channel Fused Lasso (MCFL). Specifically, we introduce a two-channel structure to implement motion detection. First, low-rank matrix decomposition is performed on the video footage along different planes. Low-rank background and sparse foreground (rough foreground candidate for the second pass) are segmented from the video sequence. Further, MCFL regularization is used for sparse signal recovery to improve the performance of the foreground mask. The proposed method is validated on different challenging videos. Sufficient experimental results show that our method is effective in a variety of challenging scenarios. Compared with the current best sparsely-based method, the performance of F-Measure improves by 0.4, 0.4, and 0.1 respectively on the I2R, BMC, and CDnet2014 datasets. Our approach is also competitive compared to the deep learning models. Our code can be obtained at https://github.com/linuxsino/MCFL.
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Organisationer och upphovspersoner

Publikationstyp

Publikationsform

Artikel

Moderpublikationens typ

Tidning

Artikelstyp

En originalartikel

Målgrupp

Vetenskaplig

Kollegialt utvärderad

Kollegialt utvärderad

UKM:s publikationstyp

A1 Originalartikel i en vetenskaplig tidskrift

Publikationskanalens uppgifter

Publikationsforum

57531

Publikationsforumsnivå

1

Öppen tillgång

Öppen tillgänglighet i förläggarens tjänst

Nej

Öppen tillgång till publikationskanalen

Delvis öppen publikationskanal

Parallellsparad

Nej

Övriga uppgifter

Vetenskapsområden

Data- och informationsvetenskap

Nyckelord

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Förlagets internationalitet

Internationell

Internationell sampublikation

Ja

Sampublikation med ett företag

Nej

DOI

10.1109/TCE.2023.3341908

Publikationen ingår i undervisnings- och kulturministeriets datainsamling

Ja