Perception-Inspired Background Subtraction
Perception-Inspired Background Subtraction
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DOI：
10.1109/tcsvt.2013.2273622
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发表时间：
2013-12
影响因子：
8.4
通讯作者：
Mahfuzul Haque;M. Murshed
中科院分区：
文献类型：
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作者：
Mahfuzul Haque;M. Murshed
开发通用且上下文不变的方法是计算机视觉中最困难的挑战之一。背景扣除 (BS) 是大多数用于前景检测的机器视觉应用中的重要前提，也不例外。由于过度依赖统计观察，大多数 BS 技术在动态无约束场景中表现出不可预测的行为，其中操作环境的特征要么未知，要么发生巨大变化。为了在不受约束的场景中实现卓越的前景检测质量，我们提出了一种称为感知启发背景减法（PBS）的新技术，它通过根据人类视觉感知的特征做出关键的建模决策来避免过度依赖统计观察。 PBS 利用人类感知启发的置信区间，在模型学习和背景-前景分类期间将观察到的强度值与另一个强度值相关联。感知启发置信区间的概念也用于识别冗余样本，从而保证背景模型中的最优样本数量。此外，PBS 根据观察到的场景动态动态改变像素级的模型适应速度（学习速率），以确保更快地适应变化的背景区域，以及更长时间地保留静止前景。对各种基准数据集进行的广泛实验评估验证了 PBS 与无约束视频分析的最新技术相比的有效性。
Developing universal and context-invariant methods is one of the hardest challenges in computer vision. Background subtraction (BS), an essential precursor in most machine vision applications used for foreground detection, is no exception. Due to overreliance on statistical observations, most BS techniques show unpredictable behavior in dynamic unconstrained scenarios in which the characteristics of the operating environment are either unknown or change drastically. To achieve superior foreground detection quality across unconstrained scenarios, we propose a new technique, called perception-inspired background subtraction (PBS), which avoids overreliance on statistical observations by making key modeling decisions based on the characteristics of human visual perception. PBS exploits the human perception-inspired confidence interval to associate an observed intensity value with another intensity value during both model learning and background-foreground classification. The concept of perception-inspired confidence interval is also used for identifying redundant samples, thus ensuring the optimal number of samples in the background model. Furthermore, PBS dynamically varies the model adaptation speed (learning rate) at pixel level based on observed scene dynamics to ensure faster adaptation of changed background regions, as well as longer retention of stationary foregrounds. Extensive experimental evaluations on a wide range of benchmark datasets validate the efficacy of PBS compared to the state of the art for unconstraint video analytics.