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.
- Top of Page
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.
- Top of Page
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.
- Top of Page
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
- Top of Page
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.
- Top of Page
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.