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

  • Accelerating a Pathway

    There is a new trend touting the transformational qualities of AI’s ability to deliver actionable data and predictive analysis that in many instances, seems to be a bit of an overpromise. The reality is that very few solutions in the cyber-physical security (CPS) space live up to this high expectation with the one exception being the new generation of Physical Identity and Access Management (PIAM) software – herein recategorized as PIAM+. Read Now

  • Protecting Your Zones

    It is game day. You can feel the crowd’s energy. In the parking lot. At the gate. In the stadium. On the concourse. Fans are eager to party. Food and merchandise vendors ready themselves for the rush. Read Now

  • Street Smarts

    The ongoing acceptance of AI and advanced data analytics has allowed surveillance camera technology to shift from being a tactical tool to a strategic business solution. Combining traditional surveillance technology with AI-based data-driven insights can streamline transportation systems, enhance traffic management, improve situational awareness, optimize resource allocation and streamline emergency response procedures. Read Now

  • The Progress of Biometrics

  • Next-Gen AI for Smart Cities

    The future of smart city technology is not being shaped in Silicon Valley — it is taking root in Dubuque, Iowa. With a population of about 60,000, this mid-sized city has become a live testbed for AI-driven traffic management thanks to a unique public-private collaboration led by Milestone Systems. Project Hafnia demonstrates how cities can transform urban mobility and safety through Responsible Technology—without costly infrastructure overhauls. Read Now

New Products

  • QCS7230 System-on-Chip (SoC)

    QCS7230 System-on-Chip (SoC)

    The latest Qualcomm® Vision Intelligence Platform offers next-generation smart camera IoT solutions to improve safety and security across enterprises, cities and spaces. The Vision Intelligence Platform was expanded in March 2022 with the introduction of the QCS7230 System-on-Chip (SoC), which delivers superior artificial intelligence (AI) inferencing at the edge.

  • AC Nio

    AC Nio

    Aiphone, a leading international manufacturer of intercom, access control, and emergency communication products, has introduced the AC Nio, its access control management software, an important addition to its new line of access control solutions.

  • A8V MIND

    A8V MIND

    Hexagon’s Geosystems presents a portable version of its Accur8vision detection system. A rugged all-in-one solution, the A8V MIND (Mobile Intrusion Detection) is designed to provide flexible protection of critical outdoor infrastructure and objects. Hexagon’s Accur8vision is a volumetric detection system that employs LiDAR technology to safeguard entire areas. Whenever it detects movement in a specified zone, it automatically differentiates a threat from a nonthreat, and immediately notifies security staff if necessary. Person detection is carried out within a radius of 80 meters from this device. Connected remotely via a portable computer device, it enables remote surveillance and does not depend on security staff patrolling the area.