Standard:
Part number:
15
Activity status:
Open
Technologies:
White papers:
Compact descriptors for video analysis
Other documents
N17121, WD 2 of ISO/IEC 15938-15 Compact Descriptors for Video Analysis
This document provides Working Draft 2 of Compact Descriptors for Video Analysis.
N17123, Description of Core Experiments on CDVA
~~The purpose of this document is to provide descriptions of six core experiments on MPEG Compact Descriptors for Video Analysis (CDVA). The previous six CEs are being continued.
The results of experiments will be discussed on the reflector before the 121st MPEG meeting.
The report of each CE should include; (1) a comparison between the tested solutions, (2) recommendation to the AhG based on the results of the CE.
N17041, CDVA Experimentation Model (CXM) 2
CDVA defines video descriptors for search and retrieval applications, specifically for visual content matching in video. Visual content matching includes matching of views of large and small objects and scenes, that is robust to partial occlusions as well as changes in view point, camera parameters, and lighting conditions. The objects of interest comprise planar or non-planar, rigid or partially rigid, textured or partially textured objects, but exclude the identification of people and faces.
N17042, Description of Core Experiments in CDVA
The purpose of this document is to provide descriptions of six core experiments on MPEG Compact Descriptors for Video Analysis (CDVA). The previous six CEs are being continued.
The results of experiments will be discussed on the reflector before the 120th MPEG meeting.
The report of each CE should include; (1) a comparison between the tested solutions, (2) a recommendation based on the results of the CE.
N17040, WD 1 of Compact Descriptors for Video Analysis
The current media and entertainment domain is characterized by increasing volumes of content, large number of delivery channels and an ever-growing need for the relevant content to be accessed on demand or published quickly. This is very difficult without appropriate tools to manage content items, including search for object instances in video, categorisation of scenes and content grouping. Thus, the ability to generate and exchange compact and standardized descriptors in an interoperable and efficient way is considered a key enabler in this domain.
N16878, Description of Core Experiments on CDVA
The purpose of this document is to provide descriptions of six core experiments on MPEG Compact Descriptors for Video Analysis (CDVA). The previous three CEs have been reviewed, and based on the responses received and the open issues they pose, one additional CE on matching and retrieval has been added, and the CEs related to deep-learning based descriptors have been split. The description of all CEs has been improved, adding more details on the experiments and expected results.
The results of experiments will be discussed on the reflector before the 119th MPEG meeting.
N16696 Description of Core Experiments in CDVA
The purpose of this document is to provide descriptions of six core experiments on MPEG Compact Descriptors for Video Analysis (CDVA) (see N15339, "Call for Proposals for Compact Descriptors for Video Analysis (CDVA) - Search and Retrieval", June 2015, Warsaw, PL). The previous three CEs have been reviewed, and based on the responses received and the open issues they pose, one additional CE on matching and retrieval has been added, and the CEs related to deep-learning based descriptors have been split.
N16695 CDVA Experimentation Model (CXM) 1.1
CDVA defines video descriptors for search and retrieval applications, specifically for visual content matching in video. Visual content matching includes matching of views of large and small objects and scenes, that is robust to partial occlusions as well as changes in view point, camera parameters, and lighting conditions.
N16509, CDVA Experimentation Model (CXM) 1.0
CDVA defines video descriptors for search and retrieval applications, specifically for visual content matching in video. Visual content matching includes matching of views of large and small objects and scenes, that is robust to partial occlusions as well as changes in view point, camera parameters, and lighting conditions. The objects of interest comprise planar or non-planar, rigid or partially rigid, textured or partially textured objects, but exclude the identification of people and faces.
N16510, Description of Core Experiments in CDVA
The purpose of this document is to provide descriptions of three core experiments on MPEG Compact Descriptors for Video Analysis (CDVA) based on the proposals submitted at 114th MPEG meeting and the submission to the 115th and 116th meeting. In addition to the CEs defined in the earlier version of this document, the submissions to the 116th proposed the use of deep-learning-based descriptors and related experiments, which have been added as CE4. CE2 on trajectory based encoding has been discontinued.
N16275, Description of Core Experiments in CDVA
The purpose of this document is to provide descriptions of three core experiments on MPEG Compact Descriptors for Video Analysis (CDVA) (see N15339, “Call for Proposals for Compact Descriptors for Video Analysis (CDVA) - Search and Retrieval”, June 2015, Warsaw (PL)) based on the proposals submitted at 114th MPEG meeting and the submission to the 115th meeting.
N16274, CDVA Experimentation Model (CXM) 0.2
CDVA defines video descriptors for search and retrieval applications, specifically for visual content matching in video. Visual content matching includes matching of views of large and small objects and scenes, that is robust to partial occlusions as well as changes in view point, camera parameters, and lighting conditions. The objects of interest comprise planar or non-planar, rigid or partially rigid, textured or partially textured objects, but exclude the identification of people and faces.
N16064, CDVA Experimentation Model (CXM) 0.1
CDVA defines video descriptors for search and retrieval applications, specifically for visual content matching in video. Visual content matching includes matching of views of large and small objects and scenes, that is robust to partial occlusions as well as changes in view point, camera parameters, and lighting conditions. The objects of interest comprise planar or non-planar, rigid or partially rigid, textured or partially textured objects, but exclude the identification of people and faces.
N15938, Results of the Call for Proposals on CDVA
At its 112th meeting, MPEG issued a call for proposals for Compact Descriptors for Video Analysis (CDVA) - Search and Retrieval. In response to the CfP, three full proposals and one partial proposal (addressing trajectory coding) were received. At the 114th meeting, these proposals were evaluated and analyzed. This document provides a summary of the full proposals received and provides the preliminary evaluation results obtained at the meeting.
Evaluation Framework for Compact Descriptors for Video Analysis - Search and Retrieval – Version 2.0
N15087, Data Requirements for Compact Descriptors for Video Analysis in Search and Retrieval Applications
This document describes data requirements for CDVS.
N14509, MPEG vision for Compact Descriptors for Video Analysis (CDVA)
Current industry systems implement the “Compress then Analyse (CtA)” paradigm, where a video is compressed using traditional video encoding methods, for further analysis.
Guided by the requests and requirements from different industries, MPEG recently started preparations to develop tools for video processing and analysis. They will address the challenge to reverse CtA into the “Analyse then Compress (AtC) paradigm to reduce the data amount for their transmission or storage and to achieve interoperability of implementations, applications and databases.