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A probabilistic approach for video stabilisation in compressed domain

机译:压缩域中视频稳定的概率方法

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摘要

Machine vision systems, which are being extensively used for intelligent transportation applications, such as traffic monitoring and automatic navigation, suffer from image instability caused by environment unstable conditions. On the other hand, by increasing the use of home video cameras which sometimes need to remove unwanted camera movement, which is created by cameraman shaking hands, video stabilisation algorithms are being considered. The video stabilisation process consists of three essential phases: global motion estimation, intentional motion estimation and motion compensation. Motion estimation process is the main time consuming part of global motion estimation phase. Using motion vectors extracted directly from MPEG compressed video, instead of any other special feature, can increase the algorithm generality. In addition, it provides the facility for integrating video stabilisation and video compression subsystems and removing the block matching phase from video stabilisation procedure. Elimination of any iterative outlier removal preprocessing and adaptive selection of motion vectors has increased speed of the algorithm. Although deterministic approaches are faster than the related probabilistic methods, they have essential problems in escaping from local optima. For this purpose, particle filters, the ability of which is considerable when submitted to non-linear systems with non-Gaussian noises, are utilised. Setting the parameters of the particle filter using a fuzzy control system reduces the incorrect intentional camera motion removal. The proposed method is simulated and applied to video stabilisation problem and its high performance on various video sequences is demonstrated.
机译:机器视觉系统已被广泛用于智能交通应用中,例如交通监控和自动导航,但由于环境不稳定条件而导致图像不稳定。另一方面,通过增加家用摄像机的使用,有时需要消除由摄影师握手产生的不必要的摄像机运动,正在考虑视频稳定算法。视频稳定过程包括三个基本阶段:全局运动估计,故意运动估计和运动补偿。运动估计过程是全局运动估计阶段的主要耗时部分。使用直接从MPEG压缩视频中提取的运动矢量代替任何其他特殊功能,可以提高算法的通用性。此外,它还提供了集成视频稳定和视频压缩子系统并从视频稳定过程中删除块匹配阶段的功能。消除任何迭代离群值去除预处理和运动矢量的自适应选择提高了算法的速度。尽管确定性方法比相关的概率方法要快,但是它们在逃避局部最优时仍存在基本问题。为此目的,使用了粒子滤波器,当将其应用于具有非高斯噪声的非线性系统时,其能力是相当大的。使用模糊控制系统设置粒子过滤器的参数可以减少不正确的故意移动摄像机的行为。对所提出的方法进行了仿真,并将其应用于视频稳定问题,并证明了其在各种视频序列上的高性能。

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