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

Jul 2, 2015

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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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Глоссарий

Версия ACC Последняя версия ACC, протестированная с камерой. Поддержка также обеспечивается и для более старых версий ACC, если в технических характеристиках не указано иное.
Аудиовход Получить аудиопоток с камеры.
Аудиовыход Отправить аудио на микрофон, подключенный к камере.
Автоматическое определение Автоматическое определение IP-адреса камеры при подключении в пределах сети LAN.
Тип сжатия Описывает тип шифрования, поддерживаемого камерой.
Тип подключения Описывает тип используемого драйвера устройства. "Native" указывает на драйвер устройства, предоставленный производителем.
Компенсация искажений Встроенная функция компенсации искажений при съемке объективами типа Fisheye или панорамными объективами.
Цифровой вход Получить цифровые входы или входы реле от камеры.
Цифровой выход Запустить цифровые выходы или выходы реле, физически подключенные к камере.
Движение Быстрый вывод информации о том, доступна ли функция записи движения для камеры.
Настройка определения движения Настройка функции определения движения в клиенте ACC.
Запись движения Поддержка записи при обнаружении движения.
PTZ Быстрый вывод информации о том, доступны ли функции PTZ для камеры.
Элемент управления PTZ Базовые движения PTZ.
Образцы/маршруты PTZ Возможность создавать и запускать образцы или маршруты PTZ в зависимости от поддерживаемой камеры.
Предустановки PTZ Создание и запуск предустановок PTZ.
Тип устройства Тип камеры.
Проверено Организация, осуществившая тестирование камеры и заявленных характеристик.
Проверенная микропрограмма Specific firmware version tested.
Производитель Blah
Модель DS-2DE2103
Тип подключения ONVIF
Тип устройства IP-камера PTZ
Compression Types H.264

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