.png){kind=icon}
返回
Interactive spatio-temporal feature learning network for video foreground detection
.png){kind=icon}
OA
AI
DOI：10.1007/s40747-022-00712-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Video foreground detection (VFD), as one of the basic pre-processing tasks, is very essential for subsequent target tracking and recognition. However, due to the interference of shadow, dynamic background, and camera jitter, constructing a suitable detection network is still challenging. Recently, convolution neural networks have proved its reliability in many fields with their powerful feature extraction ability. Therefore, an interactive spatio-temporal feature learning network (ISFLN) for VFD is proposed in this paper. First, we obtain the deep and shallow spatio-temporal information of two paths with multi-level and multi-scale. The deep feature is conducive to enhancing feature identification capabilities, while the shallow feature is dedicated to fine boundary segmentation. Specifically, an interactive multi-scale feature extraction module (IMFEM) is designed to facilitate the information transmission between different types of features. Then, a multi-level feature enhancement module (MFEM), which provides precise object knowledge for decoder, is proposed to guide the coding information of each layer by the fusion spatio-temporal difference characteristic. Experimental results on LASIESTA, CDnet2014, INO, and AICD datasets demonstrate that the proposed ISFLN is more effective than the existing advanced methods.
Keyword:
Video foreground detection
Feature enhancement
Interactive multi-scale feature
Deep learning
期刊
IF:
4.6
论文数:
2.1K
被引数:
6.6K
机构
引用论文
Contact inhibition modulates intracellular levels of miR-223 in a p27kip1-dependent manner.png)
OncotargetOAAI
OncotargetIF0
OAAI分享收藏
查看更多内容