Understanding AI in Video Surveillance

Applying human intelligence to computer programs

Many video surveillance professionals have come across the terms Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). But what do those terms mean, and how do they affect Video Surveillance?

AI, MACHINE LEARNING AND DEEP LEARNING

AI is a term that loosely refers to applying human intelligence to computer programs or allowing programs to learn over time with the goal of producing better results as they learn. Machine Learning is a technique used to achieve a level of AI, and Deep Learning is an evolution of Machine Learning. In short, Deep Learning is an advanced, more sophisticated Machine Learning technique, and both are methods of achieving a level of AI.

Application in video surveillance. In video surveillance, video analytics uses Machine Learning and Deep Learning methods to identify objects, classify them, and determine their properties.

Whenever people receive new information, our brains attempt to compare the data to similar items in order to make sense of it. This comparative approach is the same concept that Machine and Deep Learning algorithms employ.

Machine and Deep Learning algorithms differ in how they are programmed to determine what constitutes a known object. Machine Learning requires more human intervention from a programmer to establish desired parameters in order to achieve the desired outcome. Deep Learning identifies object attributes independently and may consider characteristics the programmers would not.

Machine learning versus deep learning. What do Machine Learning and Deep Learning mean for Video Analytics? Both approaches describe programming methods where a system learns based on a data set. With Machine Learning, the attributes of the data a system looks for are usually preset, or corrected for, by human programmers. For instance, the system may be programmed to delineate an object that is taller than it is wide, with limbs moving in specified ways, and so on, and label this object a “person.”

Deep Learning is considered superior to Machine Learning, in part because the programmers may not recognize the most relevant criteria. Using the previous algorithm to identify a person, a seated and stationary person may not trigger an accurate detection.

With Deep Learning, the video analytic algorithms are fed an extensive data set representing an object. This step is called training, where the algorithm trains itself to recognize a type of object. For example, the system is fed thousands of images of people of varying genders, styles of clothing, ethnic backgrounds, images taken at different angles, and more.

The algorithm figures out attributes that are similar as well as dissimilar, and also determines how to weigh the relevance of those characteristics. After analyzing thousands of images, the algorithm may calculate the majority of images include a triangular- shaped object near the upper part of the image, with two darkened oval spots near its bottom, which we would think of as a nose on someone’s face. In fact, the algorithm may have identified many other such characteristics we wouldn’t think of.

Training the system is done by the developers of the software before it is used by a consumer. The process takes a substantial amount of computing power; much more than what is required to detect and classify objects when used in the field. The result is a file that is referenced by the system to determine if a detected object matches the classification.

Because the Deep Learning process uses the machine to determine object characteristics, it has led to analytics which can provide much more granular classification. For instance, older approaches may be able to detect a person, but Deep Learning based analytics can detect whether the person is a man, woman, or child. It may also be able to detect associated characteristics of an individual as well as vehicle type or make.

Learning over time. Typically, AI in video surveillance is trained at design time and, in some cases, does not get progressively “smarter” when used in the field. Deep Learning and Machine Learning do have this capability, however, and if used, can employ analytics which can learn over time.

Typical applications may include systems that determine what is normal in a scene. For instance, a school hallway experiences a rush of traffic about every 45 minutes between class periods. During that high traffic time, the traffic is dispersed and not concentrated in any particular area.

Furthermore, it is unusual for all the people to be moving at a very high speed. If the system detects an unusual concentration of objects, it could indicate a fight broke out. If all the people are running in the same direction outside of the usual inter-class period, it could indicate an emergency situation.

SMARTER SYSTEMS, BETTER RESULTS

Video surveillance systems produce huge volumes of data. Monitoring and filtering through such vast quantities of information makes the task of quickly identifying security incidents and finding evidence more difficult than ever.

Intelligent systems using Deep Learning can help us identify evidence much more promptly and analyze video in real-time to alert system operators of suspected events, providing better results for your security program.

This article originally appeared in the May/June 2020 issue of Security Today.

Featured

  • Mall of America Deploys AI-Powered Analytics to Enhance Parking Intelligence

    Mall of America®, the largest shopping and entertainment complex in North America, announced an expansion of its ongoing partnership with Axis Communications to deploy cutting-edge car-counting video analytics across more than a dozen locations. With this expansion, Mall of America (MOA) has boosted operational efficiency, improved safety and security, and enabled more informed decision-making around employee scheduling and streamlining transportation for large events. Read Now

  • Security Industry Association Launches New “askSIA” AI Tool

    The Security Industry Association (SIA) has unveiled a brand-new SIA member benefit – askSIA, a conversational AI agent designed to help users get the most out of their SIA membership, easily access SIA resources and find the latest information on SIA’s training and courses, reports and publications, events, certification offerings and more. SIA members can easily find askSIA by visiting the SIA homepage or looking for the askSIA icon in the top left of webpages. Read Now

    • Industry Events
  • Industry Embraces Mobile Access, Biometrics and AI

    A combination of evolving workplace dynamics, technology innovation and new user expectations is changing how people enter and interact with physical spaces. Access control is at the heart of these changes. Combined with biometrics and AI, mobile access control has become increasingly crucial for deploying entry solutions that are seamless, secure and adaptive to user needs. Read Now

  • Sustainable Video Solution Delivered for Landmark City of London Office Development

    An advanced, end-to-end video solution from IDIS, with a focus on reducing waste and costs, has helped a major office development in the City of London align its security with sustainability objectives. Read Now

  • DHS to End ‘Shoes-Off’ Travel Policy

    Homeland Security Secretary Kristi Noem announced a new policy today which will allow passengers traveling through domestic airports to keep their shoes on while passing through security screening at TSA checkpoints. Read Now

New Products

  • 4K Video Decoder

    3xLOGIC’s VH-DECODER-4K is perfect for use in organizations of all sizes in diverse vertical sectors such as retail, leisure and hospitality, education and commercial premises.

  • Compact IP Video Intercom

    Viking’s X-205 Series of intercoms provide HD IP video and two-way voice communication - all wrapped up in an attractive compact chassis.

  • PE80 Series

    PE80 Series by SARGENT / ED4000/PED5000 Series by Corbin Russwin

    ASSA ABLOY, a global leader in access solutions, has announced the launch of two next generation exit devices from long-standing leaders in the premium exit device market: the PE80 Series by SARGENT and the PED4000/PED5000 Series by Corbin Russwin. These new exit devices boast industry-first features that are specifically designed to provide enhanced safety, security and convenience, setting new standards for exit solutions. The SARGENT PE80 and Corbin Russwin PED4000/PED5000 Series exit devices are engineered to meet the ever-evolving needs of modern buildings. Featuring the high strength, security and durability that ASSA ABLOY is known for, the new exit devices deliver several innovative, industry-first features in addition to elegant design finishes for every opening.