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Object tracking in surveillance videos using compressed-domain features from scalable bit-streams

机译:使用来自可伸缩比特流的压缩域功能跟踪监视视频中的对象

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摘要

Recent developments in the video coding technology brought new possibilities of utilising inherently embedded features of the encoded bit-stream in applications such as video adaptation and analysis. Due to the proliferation of surveillance videos there is a strong demand for highly efficient and reliable algorithms for object tracking. This paper presents a new approach for the fast compressed domain analysis utilising motion data from the encoded bit-streams in order to achieve low-processing complexity of object tracking in the surveillance videos. The algorithm estimates the trajectory of video objects by using compressed domain motion vectors extracted directly from standard H.264/MPEG-4 Advanced Video Coding (AVC) and Scalable Video Coding (SVC) bit-streams. The experimental results show comparable tracking precision when evaluated against the standard algorithms in uncompressed domain, while maintaining low computational complexity and fast processing time, thus making the algorithm suitable for real time and streaming applications where good estimates of object trajectories have to be computed fast.
机译:视频编码技术的最新发展带来了在诸如视频自适应和分析之类的应用中利用编码位流的固有嵌入特征的新可能性。由于监视视频的激增,强烈需要高效,可靠的对象跟踪算法。本文提出了一种利用来自编码比特流的运动数据进行快速压缩域分析的新方法,以实现监控视频中目标跟踪的低处理复杂性。该算法通过使用直接从标准H.264 / MPEG-4高级视频编码(AVC)和可伸缩视频编码(SVC)比特流中提取的压缩域运动矢量来估算视频对象的轨迹。实验结果表明,与未压缩域中的标准算法进行评估时,跟踪精度相当,同时保持了较低的计算复杂度和快速的处理时间,因此使该算法适用于必须快速计算出目标轨迹的良好估计的实时和流式应用。

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