计算机科学
人工智能
稳健性(进化)
目标检测
前景检测
特征学习
计算机视觉
融合机制
卷积神经网络
代表(政治)
水准点(测量)
融合
模式识别(心理学)
深度学习
特征(语言学)
对象(语法)
地理
法学
哲学
化学
大地测量学
基因
脂质双层融合
政治
生物化学
语言学
政治学
作者
Pei Wang,Junsheng Wu,Aiqing Fang,Zhixiang Zhu,Chenwu Wang,Shan Ren
标识
DOI:10.1016/j.dsp.2023.104046
摘要
This paper investigates the problem of effective and robust fusion representation for foreground moving object detection. Many deep learning-based approaches pay attention to network architecture based on a single modality image, ignoring the complementary mechanism of cross-modal images or only considering single-frame prediction without temporal association. We tackle these problems by proposing a fusion representation learning method for the foreground moving object detection task, which consists of two major modules: the upstream fusion representation module (FRM) and the downstream foreground moving object detection module (FODM). Unlike traditional feature aggregate methods, the FRM module is a quality-aware and online learnable fusion module which can aggregate valuable features while rejecting the harmful information in the source images. Specifically, the FODM module is a siamese convolutional neural network to detect foreground moving objects by aggregating the time-sequence images generated by FRM. Moreover, a new aligned foreground moving object detection dataset of infrared and visible images is constructed to provide a new option for benchmark evaluation. Experimental results and comparisons with the state-of-the-art on three public datasets validate the effectiveness, robustness, and overall superiority of our method.
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