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.

Description

RELATED APPLICATIONS[0001]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[0002]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[0003]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 a...

AI Technical Summary

Benefits of technology

This approach significantly reduces storage and bandwidth needs, enhances scalability, prolongs storage device lifespan, and ensures secure, redundant data storage, minimizing the need for frequent upgrades and reducing the risk of data loss or theft.

Problems solved by technology

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.

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.

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.

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.

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.

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