An object-based video coding framework for video sequences obtained from static cameras
An object-based video coding framework for video sequences obtained from static cameras
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DOI：
10.1145/1101149.1101289
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发表时间：
2005-11
期刊：
影响因子：
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通讯作者：
Asaad Hakeem;K. Shafique;M. Shah
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文献类型：
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作者：
Asaad Hakeem;K. Shafique;M. Shah
本文为从静态相机获得的视频提供了一个新颖的基于对象的视频编码框架。与大多数现有方法相反，所提出的方法不需要对象的显式2D或3D模型，因此一般足以满足场景中不同类型的对象。所提出的系统检测并跟踪场景中的对象，并使用增量主组件分析（IPCA）在线学习每个对象的外观模型。然后，使用其学到的外观空间的最重要的主组件的系数对每个对象进行编码。由于对象数量有限的姿势之间的平稳过渡，通常有限数量的重要主组件会导致对象外观空间中的大部分差异，因此只需要少量系数即可编码对象。对象运动的刚性组件是根据其仿射参数编码的。该框架用于在监视和视频电话域中压缩视频。提出的方法在包含各种场景的视频上进行评估，例如经受遮挡，分裂，合并，进入和退出的多个对象以及不断变化的背景。还介绍了标准MPEG-7视频的结果。对于所有视频，与MPEG-2和MPEG-4方法相比，所提出的方法显示较高的峰信号与噪声比（PSNR），并提供了可比或更好的压缩。
This paper presents a novel object-based video coding framework for videos obtained from a static camera. As opposed to most existing methods, the proposed method does not require explicit 2D or 3D models of objects and hence is general enough to cater for varying types of objects in the scene. The proposed system detects and tracks objects in the scene and learns the appearance model of each object online using incremental principal component analysis (IPCA). Each object is then coded using the coefficients of the most significant principal components of its learned appearance space. Due to smooth transitions between limited number of poses of an object, usually a limited number of significant principal components contribute to most of the variance in the object's appearance space and therefore only a small number of coefficients are required to code the object. The rigid component of the object's motion is coded in terms of its affine parameters. The framework is applied to compressing videos in surveillance and video phone domains. The proposed method is evaluated on videos containing a variety of scenarios such as multiple objects undergoing occlusion, splitting, merging, entering and exiting, as well as a changing background. Results on standard MPEG-7 videos are also presented. For all the videos, the proposed method displays higher Peak Signal to Noise Ratio (PSNR) compared to MPEG-2 and MPEG-4 methods, and provides comparable or better compression.