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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. ...


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.

FIG. 8 is an image of a bird perched on a birdfeeder, in which image the bird and birdfeeder are displayed as high quality images and a background scene is displayed in low quality.

DETAILED DESCRIPTION

OF PREFERRED EMBODIMENTS

System components with like reference numerals perform the same functions in each of the embodiments of a content aware storage system described below.

FIG. 1 is a pictorial diagram depicting an embodiment of a network camera system 100 utilized in an application with local campus buildings and remote sites. Network camera system 100 is not limited to video surveillance or to the application depicted, but may be used in any network communication system. Network camera system 100 includes network cameras 102 connected to a central monitoring station 104 through a network 106 that includes a wide area network (WAN) 108 and a campus local area network (LAN) 110. Network 106 may also include a wireless network 112 that includes network cameras 102′ with wireless communication capabilities. Network 106 establishes multiple network communications paths. The following descriptions of network camera 102 apply also to network camera 102′. Network 106 is not limited to the configuration depicted, but may include various configurations and types of networks. A remote user 114 may also be connected to network cameras 102 through WAN 108. Network cameras 102 may be connected to a remote storage unit 116 (i.e., a network data store). Network camera system 100 may also include various switches 118 and routers 120 to facilitate communication over network 106.

In operation, network cameras 102 capture various fields of view and generate data representing the fields of view. Certain applications may require substantially continuous operation of network camera 102. The data are communicated to central monitoring station 104, in which a user may view images, generated from the data, depicting the fields of view. Also, the data may be communicated to remote user 114 to generate images of the fields of view. The data may be stored in remote storage unit 116 and later accessed by a user.

Network camera 102 will now be described in more detail with reference to FIG. 2. Network camera 102 includes an imaging system 202, a video processing system 204, a data storage system 206 (i.e., a local data store), a power system 208, and an input/output interface and control system 210. Network camera 102 includes a camera housing; and all or portions of systems 202, 204, 206, 208, and 210 may be contained within the housing. Imaging system 202 may include a wide variety of units for capturing a field of view and for generating video information including digital data and analog signals. For example, imaging system 202 may generate information according to NTSC/PAL formats and mega-pixel formats. Imaging system 202 may include programmable imagers, high-definition imagers, no/low light sensors, and specialized imagers that are more sensitive to certain spectrums of light. Imaging system 202 may include a scalable video codec, such as MPEG-4 SVC, and other video compression capabilities, such as H.264 compression. Power system 208 may include any system for receiving and distributing electrical power to various systems of network camera 102. Power may be DC power, including Power over Ethernet (PoE), or AC power. Input/output interface and control system 210 includes various hardware and software configurations to facilitate numerous types of communication including Internet; Ethernet; universal serial bus (USB); wireless; asynchronous transfer mode (ATM); Packet over SONET/SDH (POS); pan, zoom, tilt (PZT); and audio information. Input/output interface and control system 210 may be implemented in hardware and software to allow a user to configure operation of network camera 102.

In an alternative embodiment, as depicted in FIG. 1, a video server 122 may be used in place of network camera 102, in which multiple imaging systems 202 capturing different fields of view are connected to video server 122. Video server 122 includes, within a server housing, video processing system 204, data storage system 206, power system 208, and input/output interface and control system 210. For clarity, network camera 102 will be referred to in the following descriptions, but the following descriptions are also applicable to situations in which multiple imaging systems 202 are connected to video server 122.

Content Aware Storage First Embodiment

A first embodiment of network camera 102 is described in more detail with reference to FIG. 3. Video processing system 204 includes a rules based engine 302, video analytics 304, and a storage management system 306, some or all of which may be implemented in software. Video analytics 304 includes video analytics software operating in a video analytics processor. Although video analysis and other video processing described in the following embodiments are performed by video processing system 204, video data may also be supplied from network camera 102 to a network-connected video processor, such as a video server (not shown), that performs all or part of the video analysis and other video processing described below. In other words, video analysis and processing may be distributed throughout network camera system 100. Video processing system 204 may also include video encryption capabilities to prevent unauthorized viewing of video information. Imaging system 202 captures a field of view and generates video data representing the field of view. Imaging system 202 may be programmable and may be capable of producing multiple quality levels of video data, including higher quality (HiQ) video data and lower quality (LowQ) video data. A quality level refers to multiple video parameters including resolution, frame rate, bit rate, and compression quality. For example, HiQ video data may represent D1 resolution video recorded at 30 frames-per-second (fps) and LowQ video data may represent CIF resolution video recorded at 5 fps. HiQ and LowQ video data are not limited to the parameters above. HiQ video data may represent D1 resolution video recorded at a lower frame rate—for example, 15 fps. In general, HiQ video data are video data that represent higher quality video than LowQ video data. HiQ video data are characterized by large storage requirements, and LowQ video data are characterized by small storage requirements. Imaging system 202 may produce more than two quality levels of video data. Imaging system 202 may be capable of producing different quality levels for different portions of a field of view within a video frame. For example, imaging system 202 may generate HiQ quality video data representing a person in the field of view while simultaneously generating LowQ video data representing background scene images of the field of view. As a further example, FIG. 8 depicts a bird perched on a birdfeeder in high resolution while the background scene is represented in low resolution.

Imaging system 202 communicates video data to video analytics 304. Video analytics 304, via the video analytics engine, analyzes the video data produced by imaging system 202 to detect whether a predefined event or object of interest is being captured by imaging system 202. The video data analyzed by video analytics 304 is preferably HiQ video data. Video analytics 304 generates metadata that describe the content of video data. The metadata produced by video analytics 304 may be a textual and semantic description of the content of the video.

Events and objects of interest may be programmed by a user and specified in an XML definitions file. The definitions file and video analytics 304 may be updated periodically, and definition files may be shared between video analytics 304 of different network cameras 102 within network camera system 100. Video analytics 304 of different network cameras 102 may have different analytic capabilities. Multiple events of interest may be defined, and more than one event of interest may occur at a particular time. Also, the nonoccurrence of one event leaves open the possibility of the occurrence of a second event. The metadata may be supplied to data storage system 206 and remote storage unit 116 for storage. The metadata representing an arbitrary frame n can be associated with video data representing frame n. Thus, the metadata may be searchable to allow a user to efficiently search and semantically browse large video archives, whether stored locally or remotely.

An event of interest that video analytics 304 detects may be as simple as motion in the field of view. Video analytics 304 may also implement blob detection (e.g. detecting a group of moving pixels as a potential moving object, without identifying what type of object it is), lighting change adjustment, and geometric calibration based on object size in the field of view to distinguish objects based on types. For example, video analytics 304 may be able to classify an object as a human being, a vehicle, or another type of object and be able to recognize an object when the object appears in any portion within the field of view of network camera 102. Furthermore, video analytics 304 may be able to recognize certain identifiable features of an object such as, for example, human faces and vehicle license plates. Video analytics 304 may be able to recognize when imaging system 202 is capturing a new object and assign a unique object ID to the new object. Video analytics 304 may be able to recognize the speed and trajectory at which an object moves. Video analytics 304 may be able to recognize events such as perimeter intrusion, object movement in a particular direction, objects approaching one another, a number of objects located in a specified area, objects left behind, and object removal. Video analytics 304 can also recognize specific locations, or coordinates, within the field of view where an event or object of interest is being captured, or a combination of objects and events, as defined by a rule.

When video analytics 304 detects an event or object of interest within the video data, video analytics 304 generates metadata that correspond to the event or object of interest and supplies the metadata to rules based engine 302. Rules based engine 302 includes rules that associate events or objects of interest, specified in the metadata, to specific actions to be taken. The actions associated with the rules may be to perform, for example, one or more of the following: store HiQ or LowQ video data in data storage system 206, store HiQ or LowQ video data in remote storage unit 116, stream HiQ or LowQ video data to central monitoring station 104 or remote user 114, generate and send a short video clip file of the event of interest to central monitoring station 104 or remote user 114, send an alert (e.g., instructions to generate one or both of a visual display and an audible sound) to central monitoring station 104 or remote user 114, store video data in data storage system 206 for X period of time. For example, a user may define the following rule: when a human being enters a defined perimeter, store in data storage system 206 HiQ video data representing the intrusion, alert central monitoring station 104 of the intrusion, generate a short video clip of the intrusion and send the video clip to central monitoring station 104, and store in remote storage unit 116 HiQ video data representing the intrusion. Or, a user may define the following rule: when no event or object of interest is being captured, store in data storage system 206 LowQ video data and send no video data to central monitoring station 104. Because video analytics 304 can detect various objects and events, a wide variety of rules may be defined by a user and each rule can have different storage quality settings. Also, because multiple events of interest may occur simultaneously, a rule may correspond to a combination of events.

Storage management system 306 may control storage of video data in data storage system 206 and in remote storage unit 116. Storage management system 306 is intelligently driven by the metadata generated in video analytics 304 and the rules defined in rules based engine 302. Storage management system 306 implements the actions defined by the rules. For example, storage management system 306 communicates to imaging system 202 to generate HiQ and/or LowQ video data to be stored in data storage system 206 and remote storage unit 116. Because video analytics 304 can specify locations, or coordinates, within the field of view where an event or object of interest is being captured, storage management system 306 can communicate to imaging system 202 which portions of the field of view are to be represented with HiQ video data (portions corresponding to events or objects) and LowQ video data (remaining portions). For example, FIG. 8 depicts a scene of a bird perched on a birdfeeder. Video analytics 304 can recognize the bird and portions of the birdfeeder as the most important features of the image or as objects of interest. The bird and birdfeeder, as the objects of interest, are displayed as HiQ images, while the background scene is displayed as a LowQ image. Also, imaging system 202 may be controlled such that a “windowed view” of the event or object of interest is generated by creating video data in which only the portion of the field of view corresponding to the event or object is displayed. Because HiQ and LowQ video data may be intelligently generated based upon content, events or objects of interest may be captured and stored as HiQ video data while overall storage requirements are lowered by generating LowQ video data to represent scenes in which no event or object of interest is captured.

In an alternative embodiment, imaging system 202 generates one quality level of video data to be stored in data storage system 206. Network camera 102 includes a scalable video codec, such as MPEG-4 SVC. After the video data are analyzed by video analytics 304 and stored in data storage system 206, portions of the video data may be processed using the scalable video codec to generate a second quality level (multiple quality levels may be generated using the SVC). For example, network camera 102 generates and data storage system 206 stores HiQ video data. Some time later (e.g., minutes, hours, days), the quality level of portions of the HiQ video data that represent the nonoccurrence of an event of interest are reduced to LowQ.

Storage management system 306 can also implement storage management policies that dictate how long portions of video data are stored in data storage system 206 based upon content. For example, storage management system 306 can control data storage system 206 such that important events are retained for long periods of time while less important video data are replaced with new video data within a short period of time. Storage management system 306 also controls communication of video data between sub-storage units of data storage system 206 as described below. One goal of storage management unit 306 is to minimize the frequency of writing operations from a first sub-storage unit to a second sub-storage unit.

Because video data generated by network camera 102 are stored in data storage system 206 within the camera housing of network camera 102, the video data may be more vulnerable to damage or theft. For example, if an intruder steals network camera 102, the intruder would also have in his possession the video data.

Because network camera 102 includes video analytics 304 and data storage system 206, numerous features may be implemented in system 100 to secure video data from loss or unauthorized viewing in the event that network camera 102 is stolen. For example, when an event of interest (e.g., detection of an intruder) is detected by video analytics 304, the video data representing the event of interest may be immediately streamed, or sent as video files, to remote storage unit 116, or to another network camera 102, for redundant storage. Also, shortly after the event of interest is detected, an alert and a video clip file representing the event of interest may be sent to central monitoring station 104 or remote user 114 before network camera 102 is tampered with. To prevent an intruder from viewing images captured by network camera 102, video data stored in data storage system 206 is encrypted so that the intruder cannot play back the video data. Also, video data streamed or sent as video files from network camera 102 may be encrypted to prevent unauthorized viewing.

Imaging system 202, video analytics 304, rules based engine 302, storage management system 306, and data storage system 206 cooperate to establish a content aware storage system. The content aware storage system provides a number of unique benefits not available in traditional camera systems (even those camera systems that include some form of video analytics or small capacity storage). With the content aware storage system, storage capacity needs can be greatly reduced by intelligent recognition and classification of video content. Storage capacity needs can be greatly reduced even for applications that require substantially continuous operation of network camera 102. For example, when an event of interest is captured, the content aware storage system can record the event at a HiQ level. When an event of interest is not being captured, the content aware storage system can record the video data at a LowQ level. The quality level of stored data, therefore, can be matched to the importance of the content.

Because LowQ video data may be stored when no event or object of interest is being captured, data storage system 206 may include practical storage capacity, for example 80 GB, and still be able to store video data spanning long periods of time (for example, one or two months). In comparison, a typical D1 resolution 30 fps system without content aware storage can require over 360 GB of storage for one month. Thus, typical video cameras have not been able to include a mass storage unit that can store video data spanning a long period of time. Also, because network camera 102 includes data storage system 206, video data may be stored despite network failure or network downtime due to system upgrades or maintenance. Separate networks no longer need to be set up for network cameras; network cameras can be installed on the same data network used at a particular site, saving installation costs and ongoing maintenance costs. Also, because network camera 102 includes data storage system 206, the capacity of remote storage unit 116 may be greatly reduced and remote storage unit 116 may serve primarily as backup or archival storage of important events. Also, data storage system 206 eliminates the need to include traditional DVRs and NVRs in network camera system 100.

Additionally, because network camera 102 includes data storage system 206, network bandwidth demands can be greatly reduced because network camera 102 does not have to continuously supply video data over network 106 to remote storage unit 116. Instead, network camera 102 can supply a managed amount of video data to remote storage unit 116. For example, network camera 102 may supply HiQ or LowQ video data over network 106 only when an event or object of interest is being captured. For example, events or objects of interest may be captured only ten percent or less of the time in a typical camera system. During the other 90% of the time, a user may choose to send only LowQ video data over network 106, or to send no video data at all. For wireless network 112, because network bandwidth demands are lower, more wireless network cameras 102′ can be added to wireless network 112.

Because video analytics 304 can detect when an event or object of interest is being captured, the data and metadata associated with the event or object of interest can be automatically archived in remote storage unit 116 to provide added redundancy and fault tolerance. Transmission of alarm information and video data to central monitoring station 104 or remote user 114 may also be prioritized based on importance of the video content.

Also, because of the content aware storage system, users can categorize different events or objects of interest by assigning priority values. The video data associated with the events or objects of interest can be stored intelligently in data storage system 206 for preset time periods that vary based upon the priority values. For example, less important events may be deleted after one month, but more important events may be stored for three months, six months, or a year. Also, when combined with the scalable video codec capabilities of imaging system 202, video data can be retained in data storage system 206 but reduced according to different resolutions and frame rates based upon the video content so that the video data take up less space.

Because video analytics 304 generates metadata that can be stored in data storage system 206 and remote storage unit 116, access to video data stored in data storage system 206 and remote storage unit 116 may be controlled based on content. Also, access to live video data may be controlled as metadata is created corresponding to the video data. As depicted in FIG. 4, video processing system 204 may include an access control management unit 402, which is preferably implemented in software. According to rules in rules based engine 302, different content security levels are assigned to different events or objects of interest so that access to video data may be controlled according to content. Also, different users have one or more security levels assigned to them—the security levels corresponding to one or more of the content security levels. Access control management unit 402 controls access to stored video data such that a user may access only the video data that include a content security level corresponding to the user\'s security level. Security managers, for example, may access video data flagged for security breaches or threats, but may be prevented from accessing video data that have been captured for business or marketing purposes. Likewise, marketing personnel may access video data identified for their applications but not access video security data. Policies on video encryption may also be controlled based upon content.

The content aware storage system may also intelligently distribute stored video data to maximize the available storage capacity. For example, to meet its storage needs, a first network camera 102 may need only one-half of the capacity of its data storage system 206, while a second network camera 102 may require more storage capacity than the capacity of its data storage system 206. Video data from the second network camera 102 may be supplied over network 106 to the first network camera 102 to be stored therein. Because data storage system 206 of one network camera can store data of another network camera, the total storage capacity in the system 100 can be maximized and redundant storage of important data may be distributed throughout system 100. Maximizing total storage capacity and distributing redundant storage of important data make the important data more immune to tampering or failure. Additionally, storage transfer could take place at low bandwidth times of the day.

The content aware storage system also allows network camera system 100 to be easily scalable. In conventional systems, as the number of cameras increases, storage capacity must increase by adding units to a remote storage facility. Moreover, processing power must increase by adding units to a central processing facility. With a content aware storage system, remote storage and processing facilities do not require upgrades as network cameras 102 are added to network camera system 100. Instead, each network camera 102 contains its own storage capacity (via data storage system 206) and processing power (via video processing system 204). Thus, when a network camera 102 is added to network camera system 100, the storage capacity and processing power increase simultaneously.



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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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