Moving object detection in the H.264/AVC compressed domain for video surveillance applications

Poppe, Chris; Bruyne, Sarah De; Paridaens, Tom; Lambert, Peter; Walle, Rik Van de

doi:10.1016/j.jvcir.2009.05.001

Cited by 64 publications

(27 citation statements)

References 12 publications

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“…This is done by counting the number of FG neighbors and comparing this number to a parameter N . The optimal value of this parameter was experimentally determined to be N = 4 for all sequences, which is consistent with a spatial filter in H.264/AVC [4]. The temporal filter then further reduces the amount of misclassified blocks by filtering out blocks that are labelled as FG for only one frame.…”

Section: Methodsmentioning

confidence: 89%

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Moving object detection in the HEVC compressed domain for ultra-high-resolution interactive video

Praeter

Vyver

Kets

et al. 2017

2017 IEEE International Conference on Consumer Electronics (ICCE)

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Section: Methodsmentioning

confidence: 89%

“…After the thresholding process, the detection is further augmented by applying a spatiotemporal filter as was done by Poppe et al for object detection in the H.264/AVC compressed domain [4]. Spatiotemporal filtering is a combination of both spatial and temporal filtering.…”

Section: Methodsmentioning

confidence: 99%

Moving object detection in the HEVC compressed domain for ultra-high-resolution interactive video

Praeter

Vyver

Kets

et al. 2017

2017 IEEE International Conference on Consumer Electronics (ICCE)

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“…One of the prominent works on moving-object detection in H.264/AVC compressed domain was reported by Poppe et al [60]. Almost all the related works till that time heavily depended on MVs.…”

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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“…More information on this detection technique can be found in [21]. Since this module works in the compressed domain, no color or texture information is available, so there is no need to use a detailled and advanced metadata standard like MPEG-7.…”

Section: Video Analyticsmentioning

confidence: 99%

Semantic web technologies for video surveillance metadata

Poppe

Martens

Potter

et al. 2010

Multimed Tools Appl

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Video surveillance systems are growing in size and complexity. Such systems typically consist of integrated modules of different vendors to cope with the increasing demands on network and storage capacity, intelligent video analytics, picture quality, and enhanced visual interfaces. Within a surveillance system, relevant information (like technical details on the video sequences, or analysis results of the monitored environment) is described using metadata standards. However, different modules typically use different standards, resulting in metadata interoperability problems. In this paper, we introduce the application of Semantic Web Technologies to overcome such problems. We present a semantic, layered metadata model and integrate it within a video surveillance system. Besides dealing with the metadata interoperability problem, the advantages of using Semantic Web Technologies and the inherent rule support are shown. A practical use case scenario is presented to illustrate the benefits of our novel approach.

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Moving object detection in the H.264/AVC compressed domain for video surveillance applications

Cited by 64 publications

References 12 publications

Moving object detection in the HEVC compressed domain for ultra-high-resolution interactive video

Moving object detection in the HEVC compressed domain for ultra-high-resolution interactive video

A survey on compressed domain video analysis techniques

Semantic web technologies for video surveillance metadata

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