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Compressed Domain Moving Object Detection Based on CRF

Alizadeh, M ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/TCSVT.2019.2895921
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. 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. © 1991-2012 IEEE
  6. Keywords:
  7. Compressed domain ; Conditional random field ; Moving object ; Object recognition ; Random processes ; Video recording ; Video signal processing ; HEVC ; High-efficiency video coding ; Moving objects ; Moving-object detection ; Potential function ; Video sequences ; Object detection
  8. Source: IEEE Transactions on Circuits and Systems for Video Technology ; Volume 30, Issue 3 , 2020 , Pages 674-684
  9. URL: https://ieeexplore.ieee.org/document/8629009