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Use of video camera analytics for content aware detection and redundant storage of occurrences of events of interest

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Title: Use of video camera analytics for content aware detection and redundant storage of occurrences of events of interest.
Abstract: Video analytics and a mass storage unit are contained in a camera housing of a video camera. Video data representing a field of view of a scene observed by the camera are stored in the mass storage unit. The video analytics analyzes video data produced by the camera and detects whether there is an occurrence of a defined event of interest. A video clip of the scene representing the event of interest is sent to a remote storage unit. Since only about 1% of security video data is reviewed, storing only video data representing events of interest remotely, while storing more complete video data of the scene observed by the camera local to the camera, reduces the remote storage capacity and bandwidth demand for the video system. Remote redundant storage of events of interest also provides higher reliability and fault tolerant storage for the video data that are most important. ...


Browse recent Stoel Rives LLP - Pdx patents - Portland, OR, US
Inventors: Douglas H. Marman, Mahesh Saptharishi
USPTO Applicaton #: #20110043631 - Class: 348143 (USPTO) - 02/24/11 - Class 348 


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The Patent Description & Claims data below is from USPTO Patent Application 20110043631, Use of video camera analytics for content aware detection and redundant storage of occurrences of events of interest.

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RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 12/105,871, filed Apr. 18, 2008, and claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Application No. 61/033,290, filed Mar. 3, 2008.

TECHNICAL FIELD

This disclosure describes a video imaging system that intelligently recognizes the content of video data, reduces system storage and bandwidth capacity demands, and prolongs the operational lifespan of video data mass storage units.

BACKGROUND INFORMATION

Network camera systems, for example network surveillance camera systems or IP camera systems, have existed for a number of years but have undergone relatively slow industry adoption. Compared to traditional analog camera systems, network camera systems offer advantages such as accessibility, integration, low installation costs, scalability, and an ability to move to higher resolution video. Data produced by network cameras, however, demand large amounts of bandwidth and storage capacity.

Bandwidth problems associated with network camera systems have lead to more complex camera networks that include an increased number of switches and, in some cases, complete alternative data paths. Storage problems associated with network camera systems become magnified as video resolution and the number of cameras in a system increase. For example, a single standard D1 resolution camera using MPEG-4 compression and operating at 30 frames-per-second (fps) can require 360 gigabytes (GB) of storage for video data representing one month of video data. A camera system with 1000 cameras, therefore, would require 360 terabytes (TB) of storage for data spanning one month. This example demonstrates a huge cost and facility management challenge presented with network camera systems, especially where mega-pixel resolution is desired and where applications require six months or a year of video data storage. Due to the problems identified, most network video data are not recorded at full quality, but are recorded at lower resolutions and frame rates. Because typical high resolution cameras generate video data requiring a large amount of storage resources within a short period of time, it is impractical for a typical camera to include a self-contained storage unit, such as a hard drive, that is able to store a significant amount of video data.

Typical storage architecture of network camera systems is configured with central storage similarly to traditional analog systems. The architecture includes centrally located digital video recorders (DVRs) or network video recorders (NVRs) connected through a network to IP cameras. The typical architecture for IP cameras is inadequate for a number of reasons. If, for example, the network fails or is made nonoperational for maintenance or any other reason, all video is lost and can never be retrieved. Numerous (e.g., many dozens of) cameras streaming across the network to a central storage device place severe bandwidth demands on the network. Moreover, 99% of the bandwidth used is wasted because typically less than 1% of the video is ever accessed for review. Additionally, typical network camera systems often lack storage scalability such that, as network camera systems expand, central storage systems require “forklift” upgrades.

Another problem with typical video data storage configurations is that many applications require storage devices to continuously run. Such continuous operation causes the storage devices to fail after three to five years of operation. Unless archived or stored redundantly, data on failed storage devices become lost. The need to replace storage devices, therefore, becomes a significant concern and maintenance issue.

Recently, some network camera systems have implemented video analytics processing to identify when important events (such as object movement) are being captured by a video camera. Video analytics has been primarily used to alert security of potential unwanted events. Most video analytics is performed by a central processor that is common to multiple cameras, but some video cameras have built-in video analytics capabilities. These video cameras with built-in analytics, however, have not included large capacity storage due to the large storage requirements of the video data generated by the camera and the traditional approach of centralized storage. Also, there are some cameras configured without built-in video analytics but with built-in small storage capacity that is insufficient to serve as a substitute for traditional DVRs and NVRs. Moreover, if the video data are stored only in the camera, the stored video data are vulnerable to attack or being stolen.

Therefore, a need exists for a network camera system that produces high quality video data, requires less storage capacity and network bandwidth, meets IT standards, is easily scalable, and operates for a longer period of time without storage device replacement.

SUMMARY

OF THE DISCLOSURE

The disclosed preferred embodiments implement methods and systems of content aware storage of video data produced by a video camera, which includes a camera housing and is adapted for connection to a network communication system. The video data produced represent a field of view of a scene observed by the video camera. Video analytics and a mass storage unit are contained in or form part of the camera housing. The video analytics analyzes the video data produced by the video camera and detects whether there is an occurrence of an event of interest. The video data representing the field of view of the scene observed by the video camera are stored in the mass storage unit. The stored video data include video data of a first quality and video data of a second quality. The first quality represents the occurrence in the field of view of the event of interest detected by the video analytics, and the second quality represents nonoccurrence in the field of view of the event of interest detected by the video analytics. By storing video data in the mass storage unit contained in or forming part of the camera housing, the majority of network bandwidth requirements are eliminated because the video data need not be streamed across the network for storage purposes.

The implementation described above reduces video data storage and network bandwidth requirements of a distributed network video surveillance system that includes network communication paths between network video imaging devices and network video data stores. In such surveillance system, the network video imaging devices produce video data representing fields of view of scenes under observation by the video imaging devices, and the network video data stores store video information corresponding to the video data produced by the network video imaging devices. Each of multiple ones of the network video imaging devices is associated with a content-aware video data storage system that is capable of selective storage of video data produced by its associated network video imaging device. The content-aware video data storage system includes video analytics that analyzes the content of the video data and local video data stores that store portions of the video data in response to the analysis by the video analytics. Video data corresponding to the portions of video data are delivered through the network communication paths to the network video data stores to provide a managed amount of video data representing at a specified quality level the fields of view of the scenes. The managed amount of the video data consumes substantially less network bandwidth and fewer data storage resources than those which would be consumed by delivery to the network video stores the video data produced by the network video imaging devices at the specified quality level and in the absence of analysis by the video analytics. While video surveillance applications are of particular interest, the above approach is applicable across a wide variety of video applications.

Additional aspects and advantages will be apparent from the following detailed description of preferred embodiments, which proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an embodiment of a network camera system.

FIG. 2 is a high level block diagram of a network camera of FIG. 1.

FIG. 3 is a block diagram depicting the imaging system, video processing system, and data storage system of FIG. 2 according to a first embodiment.

FIG. 4 is a block diagram depicting an access control management unit operating in the video processing system of the first embodiment of FIG. 3.

FIG. 5 is a block diagram depicting a second embodiment of the imaging system, video processing system, and data storage system of FIG. 2.

FIG. 6 is a block diagram depicting portions of the video processing system of the second embodiment.

FIG. 7 is a block diagram representing a memory buffer unit and a hard drive storage unit of data storage system.



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stats Patent Info
Application #
US 20110043631 A1
Publish Date
02/24/2011
Document #
12940829
File Date
11/05/2010
USPTO Class
348143
Other USPTO Classes
386224, 386E05003, 348E07085
International Class
/
Drawings
9


Fault Tolerant


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