Yao Guangle, Lei Tao, Zhong Jiandan, Jiang Ping, Jia Wenwu
Institute of Optics and Electronics, Chinese Academy of Sciences, P.O. Box 350, Shuangliu, Chengdu 610209, China.
School of Optoelectronic Information, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu 610054, China.
Sensors (Basel). 2017 Aug 24;17(9):1945. doi: 10.3390/s17091945.
Background subtraction (BS) is one of the most commonly encountered tasks in video analysis and tracking systems. It distinguishes the foreground (moving objects) from the video sequences captured by static imaging sensors. Background subtraction in remote scene infrared (IR) video is important and common to lots of fields. This paper provides a Remote Scene IR Dataset captured by our designed medium-wave infrared (MWIR) sensor. Each video sequence in this dataset is identified with specific BS challenges and the pixel-wise ground truth of foreground (FG) for each frame is also provided. A series of experiments were conducted to evaluate BS algorithms on this proposed dataset. The overall performance of BS algorithms and the processor/memory requirements were compared. Proper evaluation metrics or criteria were employed to evaluate the capability of each BS algorithm to handle different kinds of BS challenges represented in this dataset. The results and conclusions in this paper provide valid references to develop new BS algorithm for remote scene IR video sequence, and some of them are not only limited to remote scene or IR video sequence but also generic for background subtraction. The Remote Scene IR dataset and the foreground masks detected by each evaluated BS algorithm are available online: https://github.com/JerryYaoGl/BSEvaluationRemoteSceneIR.
背景减除（BS）是视频分析和跟踪系统中最常见的任务之一。它能从静态成像传感器捕获的视频序列中区分出前景（移动对象）。远程场景红外（IR）视频中的背景减除在许多领域都很重要且常见。本文提供了一个由我们设计的中波红外（MWIR）传感器捕获的远程场景红外数据集。该数据集中的每个视频序列都标识了特定的背景减除挑战，并且还提供了每一帧前景（FG）的逐像素真实情况。针对这个提出的数据集进行了一系列实验来评估背景减除算法。比较了背景减除算法的整体性能以及处理器/内存需求。采用了适当的评估指标或标准来评估每个背景减除算法处理该数据集中所呈现的不同类型背景减除挑战的能力。本文的结果和结论为开发用于远程场景红外视频序列的新背景减除算法提供了有效的参考，其中一些不仅限于远程场景或红外视频序列，而且对背景减除具有通用性。远程场景红外数据集以及每个评估的背景减除算法检测到的前景掩码可在线获取：https://github.com/JerryYaoGl/BSEvaluationRemoteSceneIR 。
