Moving Region Segmentation From Compressed Video Using Global Motion Estimation and Markov Random Fields

Meng, Yue; Bajić, Ivan V.; Saeedi, Parvaneh

doi:10.1109/tmm.2011.2127464

Cited by 41 publications

(9 citation statements)

References 19 publications

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“…Chen et al [16] put forward an unsupervised segmentation algorithm using global motion estimation and Markov random field (MRF) classification. First, MVs were compensated from global motion and quantized into several representative classes to remove camera motion, from which MRF priors were estimated.…”

Section: H264/avc (Mpeg-4 Part 10)mentioning

confidence: 99%

A survey on compressed domain video analysis techniques

Babu

Tom

Wadekar

2014

Multimed Tools Appl

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Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.

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Section: H264/avc (Mpeg-4 Part 10)mentioning

confidence: 99%

A survey on compressed domain video analysis techniques

Babu

Tom

Wadekar

2014

Multimed Tools Appl

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“…Hence, the motion vectors have some relationship with the global motion [10]- [12]. A global motion estimation method is proposed based on randomly selected MV groups from motion vector field with adaptive parametric model determination [5].…”

Section: Compressed Domain Based Gmementioning

confidence: 99%

Global Motion Estimation and Its Applications

Qian¹

2012

Video Compression

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“…If the detected object location is not precise. The positive samples for tracking with the MIL framework [3] [4]. This paper demonstrates the object tracking with MRF algorithm combined with ODFS algorithm in the compresses domain.…”

Section: Introductionmentioning

confidence: 99%