先前在博客中对常见的运动目标检测算法有写过一篇总结，详情请参考：http://blog.csdn.NET/kezunhai/article/details/8830787。本文是在校期间写的部分阅读笔记，贴出来，以供参考。
1. Real-Time Moving Object Detection for Video Surveillance
对视频帧进行4×4的Patch，然后用DCT提出系数向量（Coefficient vectors），用这些进行背景建模。（针对这篇文章，博客有写了相关的博文，可以参考：http://blog.csdn.Net/kezunhai/article/details/9258927）。
2. 2008_ICPR_Detecting global motion patterns in complex videos
该文章不针对单个目标的运动信息进行检测，而是对场景中的global motion pattern（全局运动模式）进行分析。该算法采用光流来计算计算独立流向量（ independent flow vector），从而得到motion pattern。主要应用于人流量非常大的场合，如地铁、火车站等，用于对人群的流动方向进行分析。
3. A texture-based method for modeling the background and detecting moving objects
对每个像素在一个Circular区域内的LPB直方图进行建模，该算法对光照有一定的敏感性。现今，有不少文章对LBP算子提出了变体，包括VLBP，STLBP以及SILBP，可以参考博主的相关博文以及博主百度空间的相关博文：http://hi.baidu.com/belial/item/76b416f42016df6c3c148533。
4. Moving object segmentation by background subtraction and temporal analysis
该论文的算法可以好好的分析下，然后予以实现。（思路：提出了一个光照增益因子和辐射相似性的概念，利用了时域的帧差，并考虑了全局的相关概念）。首先，算法通过帧差法提取运动目标，然后结合背景模型和区域的辐射相似性（corresponding region’s radiometric similarity）得到运动目标，背景更新采用了整个更新，即对前景区域对应的背景模型也进行了更新。
5. Object Detection Using Local Difference Patterns
Integrated Pixel-based and spatial-based approach，利用LDP（local Difference Pattern）进行了背景建模和实现前景检测。该算法对中心像素点与周围像素点的差值利用高斯进行建模，得到差值模型，然后进行运动目标的检测。该算法对光照变化具有较强的鲁棒性，因为在局部区域，光照的变化相似性，而通过差分建立的模型，能够进一步缩小光照的影响。
6. Spatial-Temporal patches for night background modeling by subspace learning
看到temproral，就有体的感觉，前面提到过VLBP，VLBP就是提取连续几帧的LBP特征合成一个特征。在本文中，作者提出Brick的概念，15×15×7bricks，即将图像分成15×15大小的块，然后在temporal domain中，连续帧中对应位置的7个块合成bricks。提取这些brick是的主特征向量eigenvectors，在subspace中对这些主特征向量进行学习，建立模型，从而实现目标检测。
7. Fusion of background estimation approaches for motion detection in non-static backgrounds
Fusing the results of short-term and long-term yields better performance, 其中 long-term是通过Intensities Histogram的方法来处理的（邻近的N帧），而short-term则采用single gaussian来处理。
8. pami_Density-based multi-feature background subtraction with SVM
在该算法中，颜色、梯度和Haar-like特征综合起来。在Pixels-wise应用KDA(Kernel density approximation)来建立背景模型，在目标检测部分则根据提取的特征利用SVM来进行分割，具有很好的鲁棒性。
9. Statistical modeling of complex backgrounds for foreground object detection
提出了一种以能量、空间和时间为特征的背景模型的贝叶斯框架。（A Bayesian framework that incorporates spectral, spatial and temporal features to characterize the background appearance is proposed）.根据统计的principal features，背景提取的Bayes decision rule is derived.
10. A framework for feature selection for background subtraction
提出了一个用于背景建模和减除的特征选择框架，对于每一个像素的特征组合采用RealBoost算法进行选择。通过一段时间的特征估计(KDE)，建立特征池。
11. Towards Robust Object Detection: Integrated Background Modeling Based on Spatio-temporal Features.
It consists of three complementary approaches: pixel-level background modeling, region-level one and frame-level one. The pixel-level background model uses the probability den-sity function to approximate background model. The PDF is estimated non-parametrically by using Parzen density estimation. The region-level model is based on the evaluation of the local texture around each pixel while reducing the effects of variations in lighting. The frame-level model detects sudden, global changes of the the image brightness and estimates a present background image from input image referring to a background model image.
12. Learning a scene background model via classification
We consider a two-level approximation scheme that elegantly combines the bottom-up and top-down information for deriving a background model in real time. The key idea of our approach is simple and effective:如果一个分类器能够在一个学习模型中确定图像块的某一区域为背景，并通过局部块间一致性的全局验证来提高the quality of solution. The overall idea is to efficiently identify background blocks from each images frame through online classification, and to iteratively integrate these background blocks into a complete model.(in a progressive manner).
13. An Efficient Region-Based Background Subtraction Technique
Color histograms, texture information, and successive division of candidate rectangular image regions are utilized to model the background and detect the motion. The proposed method combines this principle
and the Gaussian mixture background modeling to produce a new method which outperforms the classic Gaussian mixture background subtraction method. It has the advantages of filtering noise during image differentiation and providing a selectable level of detail
for the contour of the moving shapes. 根据不同大小的矩阵块，计算统计直方图和矩阵块内的方差，用这些值结合GMM进行背景建模。
14. The medial feature detector-Stable regions from image boundaryiccv11
用于特征检测的，类似于（MSR、SIFT、SURF等）
15. Real-time background subtraction in dynamic scene_ICCV09
In this paper, we formulate the problem mathematically as minimizing a constrained risk functional motivated from the large margin principle(最大间隔). It is a generalization of the one class support vector machines (1-SVMs) to accommodate spatial interactions, which is further incorporated into an online learning framework to track temporal changes. As a result it yields a closed-form update formula, a central component of the proposed algorithm to enable prompt adaptation to spatial-temporal changes. 这篇文章将分类问题翻译成一个数学问题，采用SVM来进行处理。
16. Making background subtraction robust to sudden illumination changes
Proposed a statistical illumination model, which model the ratio of intensities between a stored background images and an input images in all three channels as a Gaussian Mixture Model that accounts for the fact that different parts of the scene can be affected in different ways.（作者提供了源代码，可以考虑弄来做实验）
17. Detected motion classification with a double-background and aNeighborhood-based difference
这里应用a double model来进行背景检测，分别为long-term background(主要应用了temporal Medium Filter)和short-term background。
18. Learning color and locality cues for moving object detection and segmentation.
(Feng Liu and Michael Gleicher. Learning color and locality cues for moving object detection and segmentation. IEEE CVPR 2009, Miami Beach, Florida, USA, June, 2009. pp. 320-327)
It presents an unsupervised algorithm to learn object color and locality cues from the sparse motion information. We first detect key frames with reliable motion cues and then estimate moving sub-objects based on these motion cues using a Markow Randow Field framework. From these sub-objects, we learn an appearance model as a Color Gaussian Mixture Model. To avoid the false classification of background pixels with similar color to the moving objects, the locations of these sub-objects are propagated to neighboring frames as locality cues. Finally, robuts moving object segmentation is achieved by combing these learned color and locality cues with motion cues in a MRF framework.
Step1: Detect key frames that contain motion cues that can reliably indicate at least some part of the moving object.
Step2: From the key frames, we estimate moving sub-objects based on motion cues using MRF model.
Step3: We then learn from these sub-objects a moving object color model characterized by a GMM.
Step4: To avoid false detection of background pixels with similar color to the moving objects, we propagate the location information of sub-objects to non-keyframes as locality cues
Step5: we extract the moving object by combining these learned color and locality cues with motion cues in MRF framework.
应用Sift算法提取帧内特征，然后在帧间进行匹配，并应用Ransac估计Homography。(Find Some time to Research it intensively)
19 Robust Object Detection Via Soft Cascade
We describe a method for training object detectors using a generalization of the cascade architecture, which results in a detection rate and speed comparable to that of the best published detectors while allowing for easier training and a detector with fewer features.
20. Multi-layer background subtraction based on color and texture.
In this paper, we propose a layer-based method to detect moving foreground objects from a video sequence taken under a complex environment by integrating advantages of both texture and color features. Firstly, we integrate a newly developed photometric invariant color measurement in the same framework to overcome the illumination of LBP features. Secondly, a flexible weight updating strategy for background models is proposed to more efficiently handle moving background objects. Thirdly, a simple layer-based background modeling/detecting strategy was developed to handle the background scene changes due to addition or removal of stationary objects.
经过统计发现，由于光照变化引起的像素值的变化主要分布在沿着RGB原点的轴线上。因此可以通过比较像素点与原点的夹角来计算两者的差异。经过测试，算法在检测性能上不错，对光照有鲁棒性，对环境也有较强的适应性。
21. Efficient hierarchical method for background subtraction
In this paper, we propose a method that combines pixel-based and block-based approaches into a single framework. We show that efficient hierarchical backgrounds can be built by considering that these two approaches are complementary to each other. Contrast histogram is used as the features to describe each block(coarse-fine hierarchical method).而在pixel-based中，作者采用了没有指定固定的算法，用户可以自主选择。而在实验部分，作者选用的是gmm算法。
22. Using Partial Edge Contour Matches for Efficient Object Category Localization
In our contributions we focus on the partial matching of noisy edges to relax the constraints on local neighborhoods or on assigning entire edges as background disregarding local similarities. We formulate a category localization method which efficiently retrieves partial edge fragments that are similar to a single contour prototype. We introduces a self-containing descriptor for edges which enables partial matching and an efficient selection and aggregation of partial matches to identify and merge similar overlapping contours up to any length. A key benefit is that the longer the matches are, the more they are able to discriminate between background clutter and the object instance. In this way we lift standard figure/ground assignment to another level by providing local similarities for all edges in an image. We retrieve these partial contours and combine them directly in a similarity tensor and together with a clustering-based center voting step we hypothesize object locations.
可以通过下面的链接下载相关论文：http://download.csdn.net/detail/kezunhai/5834419