为了熟悉监控视频编码研究进展，本文将上世纪九十年代至今的相关论文做了总结，，并且提取出每篇论文的主要研究内容，以便个人写论文使用。
REFERENCES
[1] P. Gorur, B. Amrutur, “Skip decision and reference frame selection for low-complexity H.264/AVC surveillance video coding,” IEEE Transactions on Circuits and System for Video Technology(TCSVT), vol. 24, no. 7, pp. 1156-1169, Jul. 2014.
Gorur et al. [1] proposed GMM S-MD to reduce the computation cost of the detection process by using a cascade of spatial samplers that serve as rejection classifiers. The input image is initially sampled sparsely. The sampled pixels are classified as either background/foreground using the GMM(Gaussian mixture model) algorithm. The regions surrounding the foreground pixels are considered to be salient. These salient regions are further sampled using a dense sampler. The sampled pixels are segmented to verify the presence of foreground objects. MBs(Macro Blocks) that do not contain foreground pixels are included in the set of indices of MBs that contain onlybackground objects.
[2] I. Martins, L. Corte-Real, “A video coder using 3-D model based background for video surveillance applications,” International Conference on Image Processing(ICIP), vol. 2, pp. 919-923,1998.
Martins et al. [2] present a background modeling based approach for remote video surveillance applications at very low bit rates.The codec has two layers,one layer for the background using a 3-D model and a second layer for the part of the scene not represented by the background.The second layer may use conventional hybrid coding schemes.
[3] Tieyan Liu, Xudong Zhang, Yingning Peng, “A novel coding algorithm for video surveillance,” 2002 6th International Conference on Signal Processing(ICSP), vol 1, pp. 660-663,2002.
Liu et al. [3] designed a new coding algorithm for video surveillance. By use of the techniques such as context switch,inertia-based motion estimation,early-out DCT transform,region-based quantization and so on, this algorithm provides some attractive features for video surveillance.
[4] Rong Shi, Xiaofeng Li,Zaiming Li, “Efficient spatiotemporal segmentation and video object generation for highway surveillance video,” IEEE 2002 International Conference on Communications,Circuits and Systems and West Sino Expositions(ICCCAS), vol 1, pp. 580-584,2002.
Shi et al. [4] described a new procedure for spatiotemporal segmentation and video object generation of highway surveillance video.First the representation model is proposed to describe the relationship between the background and the moving objects. Based on this model we analyze the spatiotemporal information of some successive frames statistically to recover the background image.Then the moving objects are detected and the video object planes are extracted by the difference between the video sequence and the recovered background image.Finally the spatiotemporal similarity operator is used for object tracking.
[5] A. Vetro, T. Haga, K. Sumi, Huifang Sun, “Object-based coding for long-term archive of surveillance video,” 2003 International Conference on Multimedia and Expo(ICME), vol 2, pp.Ⅱ-417-20,2003.
Vetro et al. [5] considered video coding using several automatic segmentation algorithms to achieve significant increase in storage capacity.
[6] T. Nishi, H. Fujiyoshi,”Object-based video coding using pixel state analysis,” 2004 International Conference on Pattern Recognition(ICPR),vol 2, pp.306-309,2004.
Nishi et al. [6] described object-based coding by pixel state analysis. Pixel state analysis detects the foreground objects and background regions in video frames and distinguishes foreground object pixels as stationary or transient pixels.For stationary pixels,it is possible to restore the color intensity by referring to the same pixel location in the last frame.
[7] Yu Yang, D. Doermann, “Model of Object-Based Coding for Surveillance Video,” 2005 IEEE International Conference on Acoustics,Speech,and Signal Processing(ICASSP), vol 2, pp.693-696,2005.
Yang et al. [7] explored the model of potential savings of object-based coding for surveillance video. Moving foreground objects in stationary camera surveillance video are detected by a background subtraction technique and encoded with MPEG-4 object-based coding.
[8] J. Meesssen, C. Parisot, X Desurmont, J.-F. Delaigle, ”Scene analysis for reducing motion JPEG 2000 video surveillance delivery bandwidth and complexity,” 2005 IEEE International Conference on Image Processing(ICIP), vol 1, pp. I-577-88,2005.
Meesssen et al. [8] proposed a new object-based video coding/transmission system using the emerging Motion JPEG 2000 standard for the efficient storage and delivery of video surveillance over low bandwidth channels.
[9] Yi-Lum Lin, Shu-Fa Lin, H.H. Chen, Yuh-Feng Hsu,”Improving the coding of regions of interest,” 2006 IEEE International Symposium on Circuits and Systems(ISCAS), pp.4313-4316,2006.
我不但的回首，伫足，然后时光扔下我轰轰烈烈的向前奔去。
