Closing the gaps in terror vigilance: Smarter surveillance with AI and analytics

By: | Published: March 15, 2019 8:58 PM

Infiltration has always been a major concern in India, where illegal immigrants trespass the near-border areas like Assam, Nagaland, Kashmir, Manipur, Mumbai, etc. posing threat to the country.

Local governments and city authorities are increasingly looking for innovative ways to use technology and big data in a bid to make cities smarter.

Local governments and city authorities are increasingly looking for innovative ways to use technology and big data in a bid to make cities smarter. Crime, climate change, increasing pressure on traffic systems and demographic shifts are just some of the factors that combine to pose huge challenges for fast growing cities.

And the problems are only going to get bigger: Forecasts see a 12 percent increase in city populations by 2050, with two thirds of the global population predicted to live in cities by 2050. Apart from these factors, what hinders the effort to develop safer cities is the increasing border tension.

Infiltration has always been a major concern in India, where illegal immigrants trespass the near-border areas like Assam, Nagaland, Kashmir, Manipur, Mumbai, etc. posing threat to the country. North-eastern region of India, are always in a state of alert due to increasing infiltration from Indo-China and Indo-Bangladesh border. Even the most recent Pulwama terror attack has brought into light the importance of implementing high-end surveillance systems, in the near-border regions, to protect and preserve the community before any further incidents take place.

According to Sudhindra Holla, Sales Director, Axis Communications – India & SAARC, “It is almost mandatory to implement an integrated digital surveillance system, that outperforms a visual camera in the dark and are a great tool for detecting people and objects for 24/7 surveillance, based on the heat (body temperature) that always radiates from any object, vehicle or person.”

Read | The Pinaka system: A formidable addition in India’s weapon system

Getting smarter about perimeter and border protection with advanced analytics:

A major challenge in providing perimeter or border protection is not simply deploying video to help secure a perimeter or border. Rather, it is obtaining the relevant video after installation, which ultimately yields best situational awareness for the operator.

“In order to reduce the border tension, it is important to have a mix-bag of futuristic technologies like analytics, artificial intelligence (AI) and machine learning (ML), which can whittle down huge amounts of data to just the needed information, then use this information to take appropriate action in a far timelier fashion. Analytically-enhanced video also enables us to store only pertinent video as data. AI and ML applications can now inject this meta-data and begin producing what can become valuable tools across both the public and private domains,” he tells Financial Express Online.

Infusing analytics, AI and ML into security solutions has major implications for video surveillance, especially when it involves the wide expanses of border protection. “When it comes to border security, it is important to adopt intelligent technology, that not only picks out the types of images we seek, but also scans through all of the video data, selecting footage that requires further review based on physical characteristics, movement, behaviour and other criteria,” explains Holla.

To illustrate, such a system can send alerts when there is movement in restricted areas, such as along the LOC, for example. The surveillance system can then prompt a human operator to pay attention to such incidents and call for a response as necessary. It is available today and deployed in commercial surveillance systems worldwide.

Further developments will utilize AI and ML, in order that such systems can “learn” from real experiences and classify them as either benign or deserving of closer operator attention if necessary, all without human intervention. In such a case, AI and ML can become significant “force multipliers,” enabling organisations to optimize the use of security personnel. With fewer operators needed to monitor multiple video feeds simultaneously, such systems can potentially boost operator alertness over the course of a watch cycle. Additionally, keeping a close eye on major checkpoints can be done much more efficiently and effectively.

In border security, the need for predictive analysis is greater than ever. After all, many borders extend hundreds, if not thousands, of miles. Such a long distance, has unsurprisingly strained border patrol units; they recently struggled to meet their target Interdiction Effectiveness Rate. IER is the percentage of illegal border crossers who are either detained or turned away at the border during a given year. IER increased from 76 percent at the end of fiscal year 2013 to nearly 83 percent at the end of fiscal year 2016. However, in 2017, that number decreased to 78.9 percent, which means border control is finding it increasingly difficult to protect borders from illegal crossings.

Predictive analysis can be used more accurately to determine where to place border officers based on risk profiles instead of just footfall or expected entry points. Similar methods are also being used in other perimeter protection settings, such as airports, where manual driven perimeter protection methods are no longer enough.

Today, analytics is a part of everyday life, and it continues to rapidly advance, limited only by our willingness—and need—to explore new technologies to safeguard people, borders and infrastructure.

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