Avigilon’s IFSEC International 2015 Blog: Reducing Incident-impact, Damage and Loss

Jul 2, 2015


At IFSEC International 2015 tradeshow in London, UK at the ExCeL London, Avigilon’s Chief Technology Officer, Dr. Mahesh Saptharishi, discussed how combining high definition surveillance and self-learning analytics proactively mitigates the impact of incidents in progress and reduces damage and loss.

Saptharishi quoted a recent Forbes survey, commissioned by Avigilon, posing a very simple question: What percentage of your valuable assets are covered by video surveillance?

“We had some interesting answers,” said Saptharishi. “Over 58 percent of respondents said that under half of their valuable assets are covered by video surveillance. What was even more surprising was that only 4 percent of the respondents said that all their assets are covered by video surveillance.”

Another 31 percent of respondents had under a quarter of their assets covered by video surveillance.

“Why is it that video surveillance has such low penetration?” Saptharishi asked.

Survey participants gave three reasons: cost, effectiveness and false alarms.

However, Saptharishi told the audience that there really was one core reason: Effectiveness. He explained that false alarms reduce effectiveness and because companies become reluctant to pay a lot of money for a video surveillance system that can’t separate false alarms from real security breaches and other illegal activities.

“The real conversation is how to make video surveillance solutions effective,” said Saptharishi.

The key to that is solving the human factor problem.

“It is humans that actual detect and respond to incidents, and take actions to prevent them appropriately,” Saptharishi noted.

He quoted a NASA a study that showed if a person is engaged in a monotonous task like looking at live video streams, after about 20 minutes or so, they will fail to detect 90 percent of what happens in the scene.

This stat gets even worse when the surveillance image quality degrades and when a person is viewing multiple cameras at the same time. These scenarios are typical in most video surveillance situations.

So how do we solve the human factor? Saptharishi said it is about making video surveillance sharper.

“Image quality is never an excuse for ineffective video surveillance,” he added.

For example, images captured by Avigilon’s 7K HD Pro camera can capture stunning images that provide useful forensic detail in most surveillance scenarios.

In addition to being sharper, video surveillance is also about being smarter. This is where self-learning analytics comes into play.

Self-learning analytics continuously learns not just at the moment of install but as it starts detecting things in a scene and gets better over time.

“It is entirely self-calibrating, self-learning and extends the reach of your staff. Each operator now has the capacity to cover a wider area, monitor more cameras and focus on their core competency,” said Saptharishi.

Add to that, Avigilon’s self-learning analytics is based upon a sharper image, which enables users to find the events that actually matter and take the actions necessary to protect assets accurately.

“When events are detected as they are in progress, users have the opportunity to take control of it and prevent a bad outcome from happening,” Saptharishi said.

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Category: Security


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ACC Version Last version of ACC tested with camera. This also implies support for later versions of ACC unless specifically listed otherwise.
Audio Input Receive audio feed from camera.
Audio Output Send audio to speaker attached to camera.
Autodiscovery Automatic discovery of camera IP address when connected within a LAN environment.
Compression Type Describes the encoding types supported for the camera.
Connection Type Describes the type of Device Driver used. Native refers to the Manufacturer's specific device driver.
Dewarping In-Client dewarping of fisheye or panoramic cameras.
Digital Input Receive Digital or Relay inputs from camera.
Digital output Trigger digital or relay outputs physically connected to a camera.
Motion Quick display of whether Motion Recording is available on for the camera.
Motion Configuration Configuration of motion detection within the ACC Client.
Motion Recording Support for motion-based recording.
PTZ Quick display of whether PTZ functionality is available for camera.
PTZ Control Basic PTZ Movement.
PTZ Patterns/Tours Ability to create and trigger either PTZ Patterns, or PTZ Tours, depending on camera support.
PTZ Presets Create and trigger PTZ Preset positions.
Unit Type Type of camera.
Verified By Organization which tested camera and reported capabilities.
Verified Firmware Specific firmware version tested.
Manufacturer Blah
Model DS-2DE2103
Connection Type ONVIF
Unit Type IP PTZ camera
Compression Types H.264

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  • Model DS-2DE2103
  • Connection Type ONVIF
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