• While the rule-based analytics work mainly to detect intruders into areas where no one is normally present at defined times of day, the behavioral analytics works where people
  are active to detect things that are out of the ordinary.

• Varies from traditional mindset of security systems[edit] Typical alarm systems are designed to not miss true positives (real crime events) and to have as low of a false alarm
  rate as possible.

• Even when a human is directed to look at the actual location on a monitor of a subject in these conditions, the subject will usually not be detected.

• In the example of a cell tower the rare time that a service technician may need to access the area would simply require calling in with a pass-code to put the monitoring response
  “on test” or inactivated for the brief time the authorized person was there.

• This is because there will be many false alarms that may nevertheless be valuable to send to a human officer who can quickly look and determine if the scene requires a response.

• History Statement of the problem[edit] Limitations in the ability of humans to vigilantly monitor video surveillance live footage led to the demand for artificial intelligence
  that could better serve the task.

• Its use for non-security applications such as operational efficiency, shopper heat-mapping of display areas (meaning how many people are in a certain area in retail space),
  and attendance at classes are developing uses.

• [3] Given that many facilities have dozens or even hundreds of cameras, the task is clearly beyond human ability.

• It is characteristic of such programs that they are self-learning to a degree, learning, for example that humans or vehicles appear bigger in certain portions of the monitored
  image – those areas near the camera – than in other portions, those being the areas farthest from the camera.

• In addition to the simple rule restricting humans or vehicles from certain areas at certain times of day, more complex rules can be set.

• would learn it is usual for one human to throw another to the ground, in which case it would not alert on this observation.

• Where wide angle camera views were employed, particularly for large outdoor areas, severe limitations were discovered even for this purpose due to insufficient resolution.

• Behavioral analytics uniquely functions beyond simple security and, due to its ability to observe breaches in standard patterns of protocols, it can effectively find unsafe
  acts of employees that may result in workers comp or public liability incidents.

• For example, it might observe that individuals pass through a controlled access door one at a time.

• The user of the system may wish to know if vehicles drive in one direction but not the other.

• Machine vision is a series of algorithms, or mathematical procedures, which work like a flow-chart or series of questions to compare the object seen with hundreds of thousands
  of stored reference images of humans in different postures, angles, positions and movements.

• When the object of interest, for example a human, violates a preset rule, for example that the number of people shall not exceed zero in a pre-defined area during a defined
  time interval, then an alert is sent.

• Vehicles driving the wrong way into a one-way driveway would also typify the type of event that has a strong visual signature and would deviate from the repeatedly observed
  pattern of vehicles driving the correct one-way in the lane.

• Something as complex or subtle as a fight breaking out or an employee breaking a safety procedure is not possible for a rule based analytics to detect or discriminate.

• However, this still constitutes a new way of human and A.I.

• Talk-down[edit] One of the most powerful features of the system is that a human officer or operator, receiving an alert from the A.I., could immediately talk down over outdoor
  public address loudspeakers to the intruder.

• Users may wish to know that there are more than a certain preset number of people within a particular area.

• Humans watching a single video monitor for more than twenty minutes lose 95% of their ability to maintain attention sufficient to discern significant events.

• Frame rate per second and dynamic range to handle brightly lit areas and dimly lit ones further challenge the camera to actually be adequate to see a moving human intruder.

• [9] For an indoor or outdoor area where no one belongs during certain times of day, for example overnight, or for areas where no one belongs at any time such as a cell tower,
  traditional rule-based analytics are perfectly appropriate.

• Rules could be set for directional travel, object left behind, crowd formation and some other conditions.

• When multiple cameras are monitored, typically employing a wall monitor or bank of monitors with split screen views and rotating every several seconds between one set of cameras
  and the next, the visual tedium is quickly overwhelming.

• [citation needed] Earlier attempts at solution[edit] Motion detection cameras[edit] In response to the shortcomings of human guards to watch surveillance monitors long-term,
  the first solution was to add motion detectors to cameras.

• for security is known as “rule-based” because a human programmer must set rules for all of the things for which the user wishes to be alerted.

• Security contractors program the software to define restricted areas within the camera’s view (such as a fenced off area, a parking lot but not the sidewalk or public street
  outside the lot) and program for times of day (such as after the close of business) for the property being protected by the camera surveillance.

• Using statistical models of degrees of deviation from its learned pattern of what constitutes the human form it will detect an intruder with high reliability and a low false
  alert rate even in adverse conditions.

• in security functions to broadly scan beyond human capability and to vet the data to a first level of sorting of relevance and to alert the human officer who then takes over
  the function of assessment and response.

• Behavioural analytics Active environments[edit] While rule-based video analytics worked economically and reliably for many security applications there are many situations
  in which it cannot work.

• Places where people are moving and working do not present a problem.

• Also they are limited to the simple discrimination of whether an intruder is present or not.

• Many video surveillance camera systems today include this type of A.I.

• A one megapixel camera with the onboard video analytics was able to detect a human at a distance of about 350′ and an angle of view of about 30 degrees in non-ideal conditions.

• [5][6] True video analytics can distinguish the human form, vehicles and boats or selected objects from the general movement of all other objects and visual static or changes
  in pixels on the monitor.

• Advanced video motion detection[edit] The next evolution reduced false alerts to a degree but at the cost of complicated and time-consuming manual calibration.

• This caused hundreds or even thousands of false alerts per day, rendering this solution inoperable except in indoor environments during times of non-operating hours.

• Extensive video surveillance systems were relegated to merely recording for possible forensic use to identify someone, after the fact of a theft, arson, attack or incident.

• Practical application Real-time preventative action[edit] The detection of intruders using video surveillance has limitations based on economics and the nature of video cameras.

• While video surveillance cameras proliferated with great adoption by users ranging from car dealerships and shopping plazas to schools and businesses to highly secured facilities
  such as nuclear plants, it was recognized in hindsight that video surveillance by human officers (also called “operators”) was impractical and ineffective.

• Rule-based analytics reliably detect most true positives and have a low rate of false positives but cannot perform in active environments, only in empty ones.

• Studies have shown that companies typically only spend about one twenty-fifth the amount on security that their actual losses cost them.

• The monitoring officer would be alerted to look at his or her monitor and would see that the event is not a threat and would then ignore it.

• Nevertheless, security is a major expenditure, and comparison of the costs of different means of security is always foremost amongst security professionals.

 

Works Cited

[‘1. “Video Analytics – an overview | ScienceDirect Topics”. www.sciencedirect.com. Retrieved 2020-11-01.
2. ^ Green, Mary W. (1999) The Appropriate and Effective Use of Security Technologies in U.S. Schools, A Guide for Schools and Law Enforcement
Agencies, Sandia National Laboratories
3. ^ Sulman, N.; Sanocki, T.; Goldgof, D.; Kasturi, R., How effective is human video surveillance performance?, Pattern Recognition, ICPR 2008. 19th International Conference on, vol., no., pp.1,3, 8-11 Dec.
2008
4. ^ Nuechterlein, K.H., Parasuraman, R., & Jiang, Q. (1983). Visual sustained attention: Image degradation produces rapid sensitivity decrement over time. Science, 220, 327-329
5. ^ Pedro Domingos, The Master Algorithm: How the Quest for
the Ultimate Learning Machine Will Remake Our World, September 22, 2015 Basic Books
6. ^ Davies, E. R. (2012) Computer and Machine Vision, Fourth Edition: Theory, Algorithms, Practicalities Academic Press, Waltham Mass.
7. ^ Dufour, Jean-Yves,
Intelligent Video Surveillance Systems, John Wiley Publisher (2012)
8. ^ Hantman, Ken (2014) What is Video Analytics, Simply Explained
9. ^ Rice, Derek, Finding & Selling The Value of Analytics, SDM Magazine (Sept 2015) BNP Media II, Troy Michigan
10. ^
Gruber, Illy, The Evolution of Video Analytics, Security Sales & Integration magazine (August 11, 2012) Security Sales & Integration, Framingham MA
11. ^ Bressler, Martin S., The Impact of Crime on Business: A Model of Prevention, Detection & Remedy,
Journal of Management and Marketing Research (2009)
12. ^ Safety Index Report, Liberty Mutual Insurance Company (2002)
Photo credit: https://www.flickr.com/photos/melisatg/14087792403/’]