This paper aims to present a novel accurate moving object detection method based on the conditional random field (CRF) for high efficiency video coding/H.265 compressed domain video sequences. For each block, the number of consumed bits, motion vectors (MVs), and partitioning modes for a given block is extracted from the compressed bitstream. After removing outlier MVs, compensating MVs are assigned to the I-blocks based on their neighboring blocks. The information, such as MV, partitioning mode, and bit consumption, is used in the potential functions of a CRF model which is updated for every frame to detect the objects. Then, a number of standard test video sequences are used to verify the performance of the model. The results indicated that the model can offer a precision, that is more than 90% on average for the video sequences. The proposed method offers a 1.8 speedup, compared to the latest works in the compressed domain without losing the objects in the I-frames.

中文翻译：

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基于CRF的压缩域运动目标检测

本文旨在提出一种基于条件随机场 (CRF) 的新型精确运动目标检测方法，用于高效视频编码/H.265 压缩域视频序列。对于每个块，从压缩比特流中提取给定块的消耗比特数、运动矢量 (MV) 和分区模式。在移除离群值 MV 之后，补偿 MV 会根据它们的相邻块分配给 I 块。诸如 MV、分区模式和比特消耗等信息用于 CRF 模型的潜在函数，该模型每帧更新以检测对象。然后，使用多个标准测试视频序列来验证模型的性能。结果表明，该模型可以为视频序列提供平均超过 90% 的精度。