Real-time moving object detection in unconstrained scenes is a difficult task
due to dynamic background, changing foreground appearance and limited
computational resource. In this paper, an optical flow based moving object
detection framework is proposed to address this problem. We utilize homography
matrixes to online construct a background model in the form of optical flow.
When judging out moving foregrounds from scenes, a dual-mode judge mechanism is
designed to heighten the system's adaptation to challenging situations. In
experiment part, two evaluation metrics are redefined for more properly
reflecting the performance of methods. We quantitatively and qualitatively
validate the effectiveness and feasibility of our method with videos in various
scene conditions. The experimental results show that our method adapts itself
to different situations and outperforms the state-of-the-art methods,
indicating the advantages of optical flow based methods.

该研究论文提出了一种基于光流的移动物体检测框架，利用单应性矩阵在线构建光流背景模型，并设计了一个双模式判断机制来提高系统对复杂场景的自适 应性，实验证明该方法优于现有的方法，具有适应不同场景的优势。

基于光流的无约束场景实时移动物体检测

Optical Flow Based Real-time Moving Object Detection in Unconstrained  Scenes

Foreground detection has been widely studied for decades due to its
importance in many practical applications. Most of the existing methods assume
foreground and background show visually distinct characteristics and thus the
foreground can be detected once a good background model is obtained. However,
there are many situations where this is not the case. Of particular interest in
video surveillance is the camouflage case. For example, an active attacker
camouflages by intentionally wearing clothes that are visually similar to the
background. In such cases, even given a decent background model, it is not
trivial to detect foreground objects. This paper proposes a texture guided
weighted voting (TGWV) method which can efficiently detect foreground objects
in camouflaged scenes. The proposed method employs the stationary wavelet
transform to decompose the image into frequency bands. We show that the small
and hardly noticeable differences between foreground and background in the
image domain can be effectively captured in certain wavelet frequency bands. To
make the final foreground decision, a weighted voting scheme is developed based
on intensity and texture of all the wavelet bands with weights carefully
designed. Experimental results demonstrate that the proposed method achieves
superior performance compared to the current state-of-the-art results.

该论文提出了一种纹理引导加权投票（TGWV）方法，可以有效地检测伪装场景中的前景对象。该方法利用静态小波变换将图像分解成频带，并根据所有小波频带的强度和纹理设计了加权投票方案，结果表明该方法的性能优于当前的最新结果。

伪装场景下的前景检测

Foreground Detection in Camouflaged Scenes

Given a set of images of a scene taken at different times, the availability
of an initial background model that describes the scene without foreground
objects is the prerequisite for a wide range of applications, ranging from
video surveillance to computational photography. Even though several methods
have been proposed for scene background initialization, the lack of a common
groundtruthed dataset and of a common set of metrics makes it difficult to
compare their performance. To move first steps towards an easy and fair
comparison of these methods, we assembled a dataset of sequences frequently
adopted for background initialization, selected or created ground truths for
quantitative evaluation through a selected suite of metrics, and compared
results obtained by some existing methods, making all the material publicly
available.

为实现对场景背景的初始化，本文收集了一份常用于初始化背景的数据集，并选择或创建了一组基准数据进行定量评估，以此来比较现有方法的性能表现。