Video content analysis: Difference between revisions
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VCA relies on good input video, so it is often combined with video enhancement technologies such as [[video denoising]], [[image stabilization]], [[unsharp masking]] and [[super-resolution]]. |
VCA relies on good input video, so it is often combined with video enhancement technologies such as [[video denoising]], [[image stabilization]], [[unsharp masking]] and [[super-resolution]]. |
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==Functionalities== |
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This is a list of known functionalities and a short description. |
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! Function !! Description |
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| Dynamic Masking || Blocking a part of the video signal based on the signal itself, for example because of privacy concerns. |
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| [[Egomotion | Egomotion estimation]] || Egomotion estimation is used to determine the location of a camera by analyzing its output signal. |
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| Motion detection || Motion detection is used to determine the presence of relevant motion in the observed scene. |
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| Object detection || Object detection is used to determine the presence of a type of object or entity, for example a person or car. Other examples include fire and smoke detection. |
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| Recognition || [[Facial recognition system|Face recognition]] and [[Automatic Number Plate Recognition]] are used to recognize, and therefor possibly identify persons or cars. |
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| Style detection || Style detection is used in settings where the video signal has been [[Filmmaking#Production|produced]], for example for television broadcast. Style detection detects the style of the production process.<ref name="Style detection">[http://www.informedia.cs.cmu.edu/documents/snoek_ICME04.pdf Style detection], Cees G.M. Snoek et al., ''Detection of TV news monologues by style analysis'', ICME'04</ref> |
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| Tamper detection || Tamper detection is used to determine whether the camera or output signal is tampered with. |
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| [[Video tracking]] || Video tracking is used to determine the location of persons or objects in the video signal, possibly with regard to an external reference grid. |
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==Commercial applications== |
==Commercial applications== |
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VCA is a new technology. New applications are frequently found, however the track record of different types of VCA differs widely. Functionalities such as motion detection and people counting are believed to be available as [[COTS]] products with a decent track-record. |
VCA is a new technology. New applications are frequently found, however the track record of different types of VCA differs widely. Functionalities such as motion detection and people counting are believed to be available as [[COTS]] products with a decent track-record. |
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Revision as of 05:51, 7 July 2010
Video Content Analysis (VCA) is the capability of analyzing video to detect and determine temporal events not based on a single image. It is used in a wide range of domains including entertainment[1], health care, retail, automotive, transport, domotica, safety and security[2]. The algorithms can be implemented as software on general purpose machines, or as hardware in specialized video processing units.
Many different functionalities can be implemented in VCA. Video Motion Detection is one of the simpler forms where motion is detected with regard to a fixed background scene. More advanced functionalities include video tracking and egomotion estimation.
Based on the internal representation that VCA generates in the machine, it is possible to build other functionalities, such as identification, behavior analysis or other forms of situation awareness.
VCA relies on good input video, so it is often combined with video enhancement technologies such as video denoising, image stabilization, unsharp masking and super-resolution.
Functionalities
This is a list of known functionalities and a short description.
| Function | Description |
|---|---|
| Dynamic Masking | Blocking a part of the video signal based on the signal itself, for example because of privacy concerns. |
| Egomotion estimation | Egomotion estimation is used to determine the location of a camera by analyzing its output signal. |
| Motion detection | Motion detection is used to determine the presence of relevant motion in the observed scene. |
| Object detection | Object detection is used to determine the presence of a type of object or entity, for example a person or car. Other examples include fire and smoke detection. |
| Recognition | Face recognition and Automatic Number Plate Recognition are used to recognize, and therefor possibly identify persons or cars. |
| Style detection | Style detection is used in settings where the video signal has been produced, for example for television broadcast. Style detection detects the style of the production process.[3] |
| Tamper detection | Tamper detection is used to determine whether the camera or output signal is tampered with. |
| Video tracking | Video tracking is used to determine the location of persons or objects in the video signal, possibly with regard to an external reference grid. |
Commercial applications
VCA is a new technology. New applications are frequently found, however the track record of different types of VCA differs widely. Functionalities such as motion detection and people counting are believed to be available as COTS products with a decent track-record.
In many domains VCA is implemented on CCTV systems, either distributed on the camera's (at-the-edge) or centralized on dedicated processing systems. Video Analytics and Smart CCTV are commercial terms for VCA in the security domain. In the UK the BSIA has developed an introduction guide for VCA in the security domain[4].
Kinect is an add-on peripheral for the Xbox 360 gaming console that uses VCA for part of the user input.[5]
The quality of VCA in the commercial setting is difficult to determine. It depends on many variables such as use case, implementation, system configuration and computing platform. Typical methods to get an objective idea of the quality in commercial settings include independent benchmarking[6] and designated test-locations.
Academic Research
Video Content Analysis is a subset of Computer Vision and thereby of Artificial Intelligence. Two major academic benchmark initiatives are TRECVID[7] and the PETS Benchmark Data[8]. They focus on functionalities such as tracking, left luggage detection and virtual fencing. Significant academic research into the field is ongoing at the LIVS, University of Calgary, University of Waterloo, University of Kingston, Georgia Institute of Technology, Carnegie Mellon University, and The British Columbia Institute of Technology.
References
- ^ KINECT, add-on peripheral for the Xbox 360 console
- ^ VCA usage increase in British Security, BSIA report
- ^ Style detection, Cees G.M. Snoek et al., Detection of TV news monologues by style analysis, ICME'04
- ^ British Industry VCA Guide, 262 An Introduction to Video Content Analysis Industry Guide
- ^ "Project Natal 101". Microsoft. 2009-06-01. Archived from the original on 2009-06-01. Retrieved 2009-06-02.
- ^ i-Lids, Benchmarking initiative by the UK Home Office
- ^ TRECVID, Academic benchmark initiative by NIST
- ^ PETS Benchmark Data, Performance Evaluation of Tracking and Surveillance (PETS) by University of Reading
External links
- ipvideomarket.info: Commercial news outlet
- Demystifying video analytics - is "intelligent" now a dirty word?: Criteria to determine intelligence of video analytics solutions by Edward Troha, Director of Marketing for ObjectVideo
- Video Analytics: Understanding their limitations by Oliver Vellacott, CEO of IndigoVision
- Video analytics: the new magic solution? by Jeremy Kimber, Honeywell's EMEA Marketing Leader