Real-Time Foreground Segmentation for the Moving Camera Based on H.264 Video Coding Information

Hong, Wei-Di; Lee, Tien-Hsu; Chang, Pao-Chi

doi:10.1109/fgcn.2007.191

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Unlike the previous works, Hong et al [29] proposed a moving object segmentation approach in H.264 compressed domain considering moving camera scenarios. They have used cues only from the block partition modes and MVs in the compressed bit stream.…”

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

confidence: 97%

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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: 97%

“…The algorithm suits only for segmenting videos captured from the fixed cameras. Hong et al [29] processed the MVs to estimate the global motion. Due to this, their approach was capable of handling the case of moving camera as well.…”

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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“…Object motion also called local motion is the residual between the original motion and the global motion. There are some researches on foreground detection based on global motion from compressed video [7,8,9]. In this paper, we propose a motion-based approach to detect the foreground and combine luminance and chromaticity factors to refine the result from compressed video.…”

Section: Introductionmentioning

confidence: 99%

Motion-Based Foreground Extraction in Compressed Video

Zhu

Qishan

2010

2010 International Conference on Measuring Technology and Mechatronics Automation

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This paper presents a new method to distinguish the foreground from compressed videos. It demonstrates that the foreground detection can be effectively achieved by the local motion combine luminance and chromaticity factors. In order to calculate the local motion, which is estimated from the original motion and the global motion, we built a fourparameter model to compute global motion. LevenbergMarquardt algorithm is introduced to revise those parameters. For luminance and chromaticity factors, DC coefficients are employed to segment the objects, which are considered as the index of the image. Some implement results show that our algorithm provides a practical way for foreground extraction and more researches of high level video processing will be carried out based on it.

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“…A variety of methods in the fields of image processing, signal processing, and computer vision have been developed to solve aspects of the problem of unsupervised detection and tracking of arbitrary objects. These methods might be placed into a few broad categories: those that aim to distinguish the foreground regions of images from the background [33,12,65,39], segment images into distinct regions to perform localization [35,23,56], track an object over a sequence of images (after its position has been specified in an initial image) [52,43,36,16,50], track multiple objects over a sequence of images (especially when the objects interact or occlude one another) [54,18,66,32,44,19], segment a sequence of images into distinct spatiotemporal regions [10,55,61], and combine the previous methods in some way to create systems capable of both detecting and tracking specified objects [47,7,67,38,41], or of discerning which regions of a video constitute distinct, arbitrary objects and tracking these [8,9,25,49,48].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures

Neiswanger¹,

Wood²

2012

Preprint

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This paper proposes a technique for the unsupervised detection and tracking of arbitrary objects in videos. It is intended to reduce the need for detection and localization methods tailored to specific object types and serve as a general framework applicable to videos with varied objects, backgrounds, and image qualities. The technique uses a dependent Dirichlet process mixture (DDPM) known as the Generalized Polya Urn (GPUDDPM) to model image pixel data that can be easily and efficiently extracted from the regions in a video that represent objects. This paper describes a specific implementation of the model using spatial and color pixel data extracted via frame differencing and gives two algorithms for performing inference in the model to accomplish detection and tracking. This technique is demonstrated on multiple synthetic and benchmark video datasets that illustrate its ability to, without modification, detect and track objects with diverse physical characteristics moving over non-uniform backgrounds and through occlusion.

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Real-Time Foreground Segmentation for the Moving Camera Based on H.264 Video Coding Information

Abstract: Abstract

Cited by 7 publications

References 10 publications

A survey on compressed domain video analysis techniques

A survey on compressed domain video analysis techniques

Motion-Based Foreground Extraction in Compressed Video

Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures

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