In the year 2008, cameras are nearly everywhere, and wherever they aren't, they will be soon. While the reasons for installation of these cameras vary widely, they generally fall into security and safety categories. These cameras are additional "eyes" that work 24 hours a day, seven days a week. Human monitoring is being replaced by Video Analytics. Recent advances in video processing Digital Signal Processors have enabled migration of the image analysis task from central computers to autonomous intelligent cameras.
Where it started
Initially, the dominant security and surveillance technology was based on analog (NTSC/PAL) cameras. Human beings watched one or more monitors; each connected to a single camera, or switched between multiple cameras, looking for something to happen. The next step was to record the video with banks of video tape recorders. Even though this made off-line video review and archiving possible, the mechanics were unappealing. Large installations required dedicated rooms just to store their videotapes. While the video was indeed archived, review and retrieval was awkward; first find the right cassette, put it into a VCR player, then search using fast forward and rewind.
A significant advancement was the replacement of the tape-based VCR's by Digital Video Recorders (DVRs). While still connected to analog cameras, they had the ability to store the CCTV video data like any other form of digital data hard drives, digital tapes, etc. Network accessible storage made video storage, retrieval and review much easier.
Current technology for this type of system is based on Network or IP cameras. The entire video system has now been digitized with the introduction of IP Cameras with integrated web servers--stand-alone devices that allow the user to view live, high resolution, full motion video from anywhere on a computer network (bandwidth permitting), or over the Internet, using a standard web browser. They can be connected either to an ad-hoc or existing IP network. Images can be viewed and cameras managed via standard web browsers. Network-based storage resources can be set up to record the video output.
Video is high bandwidth, high volume data. A monochrome (eight bit per pixel) VGA video stream, uncompressed at 30 frames per second, requires nine megabytes of storage per second of video. Color, higher resolution and higher frame rates rapidly multiply this number. The demands on communications, computer MIPS and storage capacity can be very high. Terabytes of information-sparse data can rapidly be created and archiving all of that video may be necessary for the court case, but doesn't do much for responding to a critical situation or developing problem.
The human element
While the collection, management, and storage of all this video has been greatly improved, the analysis has, until recently, been left to humans. Human monitoring is costly and problematic. Humans do not do a very good job at monitoring for low rate of occurrence events. Most of the time, nothing is happening. Watching a video screen of an empty warehouse for an event, which may, should, never occur, is not a desirable job and one that even the most conscientious of employees will not do well. The worst case is the "blink of an eye" event that rarely, if ever, occurs, as there is an extremely high probability that a human will miss it.
Removing the weak link
These problems with human monitoring, both cost and quality, have resulted in the emerging discipline of "Video Analytics", a.k.a. "Machine Vision", "Computer Vision", or "Intelligent Video Surveillance". Video Analytics is the science of analyzing video (as opposed to a single frame) for events of interest. The "event of interest" may be as simple as motion (something in the scene changed) or as complex as detecting the signature of a smoke plume in a warehouse. On detection of the event, appropriate action can be taken--send the video to an actual human being, launch an email, call a cell phone, sound an alarm, stop a machine, etc.
While VA (Video Analytics) has typically been PC based, the advent of high performance video processing DSP systems have opened up the possibility of camera based Video Analytics, or Video Analytics on the Network Edge.
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