Complete guide to video analytics surveillance technology and solutions

What is video analytics?

Video analytics describes the process of observing and analyzing recorded video content to transform real-time information into intelligent and actionable insights. Smart video analytics for security systems use specialized AI and machine learning technologies to continuously observe video footage, with programs configured to automatically detect suspicious and anomalous events.

In operation, this enables active video security systems to identify and observe various objects and stimuli associated with security incidents without assistance from human operators. For example, video analytics systems can autonomously detect and observe vehicles, persons of interest, contraband items and unusual objects, warning staff of events that require attention.

Choosing to deploy video analytics security tools removes the need for on-site personnel to manually observe CCTV feeds permanently. Instead, AI programs ensure sites are well-observed by warning security staff of events that require their attention and response.

How does a video analytics system work?

Video analytics systems work by relying on rule-based algorithms to discern whether stimuli in live surveillance footage represent a threat. Software tools will pose and answer a series of questions in an ​“if/​then” decision tree. For example, if an object is detected, and that object is the same size and shape as a weapon, an alert will be sent to staff requesting a response.

CCTV analytics systems observe live footage by isolating individual frames and inspecting the images in sequence. Rule-based algorithms continually analyze this footage in relation to the above-mentioned decision tree, recording all changes in the form of intelligent metadata.

The recording and documentation of this data enable deep learning in video analytics, with this process helping to enhance threat detection capabilities. Over time, AI tools can learn patterns in data that then inform the operation of surveillance systems. Video analytics cameras essentially understand movement, patterns and objects associated with normal and safe daily activity, enhancing the system’s ability to identify and address real security threats.

The importance of video analytics surveillance

The efficacy of video security systems is entirely dependent on how captured video footage is utilized. While different types of CCTV cameras can be effective as a visible crime deterrent, the primary purpose of surveillance systems is to detect and address potential threats before they develop to cause harm to people or property. This idea underlines the importance of data analysis as a whole.

The real benefit of video analytics surveillance solutions is that the observance, assessment and reaction to potential threats can be automatically and continuously performed. Furthermore, AI algorithms can perform these functions much faster, more accurately and more reliably than human teams over long periods. This frees up time for staff to focus on more complicated tasks.

Enhanced visibility

Integrating advanced video analytics software into business surveillance systems ensures security staff have optimal visibility over key locations at all times. Video analytics for security surveillance cameras can be linked to a wider cloud management system, enabling security personnel to view live camera feeds remotely from a unified dashboard and receive live alerts straight to handheld smart devices. 

Developing such a solution means security teams can patrol specific locations based on real-time security information. AI systems can automatically activate wider security devices and provide additional contextual details to ensure responses are performed safely.

Instant responses

Security camera analytics systems enable stakeholders to enact instant incident responses by informing teams of anomalous events in real-time. As soon as the system detects signs of suspicious activity, live alerts can be sent to staff containing important contextual information intended to help responding teams address and resolve incidents quickly and appropriately.

Actionable insights

As surveillance video analytics tools can continuously observe and analyze key areas, using deep learning models to effectively understand patterns of normal operation, systems can be used to gain actionable insights into potential safety and productivity improvements. 

For example, advanced video analytics systems can help businesses better understand how and why previous workplace accidents and injuries occurred. AI models can analyze relevant footage to highlight irregular movements or actions that may have led to a hazardous event. Teams can then use these insights to improve active policies and develop better training programs.

Service quality

Video analytics solutions can be deployed to improve far more business processes than just on-site security. For example, in caregiving facilities, video analytics systems may be used to observe how patients and staff move throughout the premises. Findings can help to inform smart changes made to policies and systems intended to improve service speed and quality. 

Real-time video analytics can also be used to analyze behavioral information of service users. Subtle changes in a patient’s facial expressions and posture may be used to help professionals better evaluate present conditions. Video analytics cameras can highlight emotions, body language shifts and micro-expressions too subtle for staff to reliably identify.

Customer behavior

Advanced video analytics solutions are becoming increasingly popular in retail environments for their ability to help businesses better understand their customers. AI algorithms can observe the customer journey throughout retail spaces to understand how key demographics interact with certain products and the store as a whole, with findings used to enhance store layouts and the placement of products.

Vast amounts of high-quality data can be intelligently analyzed, including the average length of a customer’s visit, the average age of repeat customers and which times or days see the most or least foot traffic. Reviewing collected data can help businesses optimize operations to best address the needs of unique audiences and review the efficacy of chosen alterations.

Common types of video analytics in CCTV

While the term video analytics can be used to describe any intelligent surveillance system capable of autonomously capturing, analyzing and understanding video footage, there are many different types of video analytics tools specially designed to perform specific functions.

The following section outlines several common video analytics examples that different organizations, businesses and institutions currently use to enhance various key operations.

License plate recognition (LPR)

LPR camera systems utilize a combination of Optical Character Recognition (OCR) software and video analytics tools to discern and analyze license plate data associated with passing cars. When an object travels past the camera, video analytics tools will assess its shape, size and movement characteristics to determine whether the target is likely to be a vehicle or not.

The software will then find the location of the license plate based on stored information and previous incidents. If a plate is detected, OCR software will analyze this part of the image to extract legible text from the footage. Video analytics with machine learning capabilities can gradually learn exactly where in a live image to look for license plate data, based on the positioning of the camera and previous positive readings, to improve operational efficiency.

Facial recognition (FR)

Video analytics solutions designed to perform facial recognition functions can be used for many different purposes. FR scans can be used as access credentials to control entry to high-security locations, to observe private properties in search of known offenders or unauthorized individuals and to review historical footage in search of persons of interest.

Cameras equipped with facial recognition video analytics tools automatically map key facial features present in live or recorded images and then compare this information to a database of known individuals. Depending on the use case, a positive or negative result will trigger wider devices such as access control security systems, on-site alarms or SMS remote notification systems.

Occupancy counting

Surveillance cameras that utilize occupancy counting video analytics software are typically used to enhance building management system operations and improve service delivery in commercial environments. Stationary cameras will continuously observe select locations, with AI models positioned to autonomously detect people entering, exiting and moving around the property.

Resulting data can be used to discern live and historic occupancy levels, detect bottlenecks and inefficiencies in building layouts, and offer insights into wait times and service delivery metrics. This type of video analytics can also help building managers and business owners adjust operations in real-time, like opening or closing egresses based on occupancy levels.

Object tracking

Object tracking video analytics tools are used to identify and observe specific objects present in live or historical video footage. Systems use deep learning algorithms to discern complex patterns in large datasets, enabling cameras to identify, track and record objects of interest.

Bespoke solutions can be trained to detect multiple classes of objects, including weapons, contraband items and people, as well as subject to rule-based calculations. For example, if people are detected, video analytics tools can further examine behavioral patterns like the presence of crowds or unusual motion to better understand whether a threat is developing.

How do different industries use a video analytics system?

Given the ability and intelligence of modern video analytics solutions, different combinations of hardware and software components can be developed to address the unique challenges and requirements associated with various industries. Below are some realistic examples of how video camera analytics solutions are currently utilized to support different organizations.

Video analytics in retail

Video analytics with machine learning capabilities are becoming increasingly commonplace in retail environments. Business owners can use these tools to better understand customer behavior, improve on-site store security and boost the efficiency of building management systems.

Facial recognition cameras can be used to analyze the unique characteristics of customers, enabling teams to better understand target audiences and identify demographics they’re currently failing to attract. These same video analytics can also help with loss prevention strategies by detecting individuals who have performed past criminal activity at the store or those who are banned from entering the premises. Video analytics can also analyze how customers interact with the store, following their gaze and movements to determine the efficacy of marketing practices.

Healthcare video analytics

Video analytics in healthcare help administrators to ensure a safe environment for patients and staff. Object tracking and facial recognition cameras stationed at main access points are used to identify the presence of contraband items and unauthorized persons, with additional integrations enabling teams to program immediate responses, alerts and full-site lockdowns in their healthcare security systems.

Video analytics can also be used to detect subtle changes in a patient’s facial expressions, body language and general demeanor, providing clinicians with contextual data to support or contradict proposed diagnoses. In addition, occupancy counting analytics can help staff analyze wait times and service delivery to highlight potential inefficiencies that should be addressed.

Video analytics in law enforcement

Stationary and handheld LPR cameras help law enforcement officers detect and address vehicular crimes, as well as observe traffic flows in real-time to optimize road management operations. Video analytics can also be used to detect the presence of persons of interest in specific locations, using facial recognition and object tracking solutions to aid investigations.

Recognition tools are also used to analyze historical data about active investigations, enabling officers to review large quantities of collected video footage quickly and accurately. Alongside searching for persons of interest, video analytics algorithms can scan for specific objects in CCTV footage that may provide clues or extra evidence to support investigations.

Smart city management

Video surveillance analytics have proved pivotal in the development of modern smart cities, enabling city planners and officials to optimize essential services in response to high-quality data insights. AI security cameras stationed at intersections can be used to observe traffic patterns and adjust traffic light sequences to ease congestion while providing traffic updates to help transportation and sanitation officials adjust service schedules in line with the most efficient possible routes.

Video analytics in manufacturing

Smart video analytics cameras positioned to observe production lines are used to help staff identify anomalies in materials and products, helping organizations improve quality control operations. These same manufacturing security systems can also observe productivity and downtime to ensure lines are functioning optimally, producing data insights to help managers identify inefficiencies.

Video analytics can also analyze machinery during production to identify any potential issues that may need to be addressed, such as overheating. Records of machine operation can equally be logged and analyzed to inform maintenance processes, ensuring all equipment is appropriately serviced.
 

The pros and cons of video surveillance analytics

When choosing whether or not to develop a bespoke video analytics surveillance system, it’s important to consider the benefits of video analytics technology equally alongside its current limitations.

Key benefits

• Security: Video analytics surveillance for security staff provides them with real-time visibility over all areas of the facility, ensuring threats are identified and addressed quickly and appropriately.

• Decision-making: Smart analytics systems compile large amounts of important data continuously, providing actionable real-time insights to inform key business decisions.

• Efficiency: Handing over some surveillance responsibilities to video analytics systems ensures fast and accurate data processing, reducing the need for manual observance.

• Continuous learning: Deep learning in video analytics enables systems to improve over time, with tools able to spot patterns and inconsistencies that humans may miss.

Potential disadvantages

• Data quality: Video analytics surveillance solutions are only as effective as the data they’re fed. If datasets are inconsistent or inaccurate, solutions may not offer the required functionality.

• Regulatory compliance: As video analytics technology relies on the analysis of identifiable data, privacy concerns can be an issue, especially in industries with strict regulations.

• Maintenance: Efforts must be made to continuously review, update and maintain video analytics tools to ensure systems do not become vulnerable to novel threats.

Considerations when choosing video analytics technology

Image quality

To ensure video analytics surveillance solutions function as accurately and effectively as possible, teams must prioritize high-quality security cameras designed to produce high-definition images. Ideal cameras will also be well-suited to the environment that is to be observed. For example, an outdoor commercial security camera installation will require durable hardware featuring night-vision and low-light settings.

Integration possibilities

Working with a security integrator can help your business develop integrated surveillance systems, whereby insights produced by video analytics tools can inform the operation of wider devices. This can enable businesses to program automated responses as well as save money and time. Look for video analytics surveillance software that’s compatible with all existing or proposed security devices and management systems to help teams develop advanced video analytics installations.

Storage and management 

Collected raw data and analytical insights will need to be stored in a secure and convenient management system. Leaders will need to consider the pros and cons of server-based and cloud-based solutions, including appropriate cybersecurity and maintenance considerations.

Data privacy

All identifiable information collected by video analytics cameras and video management software solutions will need to be stored in compliance with relevant data privacy regulations. Laws like the GDPR, HIPAA and CCPA must be followed, with these requirements outlining how organizations are permitted to collect, process, store and dispose of sensitive data used in video surveillance.

Best practices for implementing CCTV video analytics

1. Select software: Consider priorities to identify appropriate software. Will object tracking, LPR systems, people counting or facial recognition solutions be required? Some installations will require a combination of tools, others may need a dedicated solution.

2. Define objectives: What data does the system need to collect? Program chosen solutions to search for required stimuli, this could include contraband items, people, vehicles or signs of suspicious activity in the form of anomalous behaviors/​patterns.

3. Set parameters: Fine-tune video analytics surveillance tools in line with unique environmental and organizational requirements. This includes setting thresholds, filters and rules to govern the types of events and stimuli that installed systems should respond to.

4. Test analytics results: Continually review the operation of video analytics technology to ensure they’re operating as intended. Consider factors like accuracy, consistency and false alarms, making notes of these findings to help inform future improvements.

5. Observe and optimize: Frequently reassess the efficacy of the system and apply any necessary changes identified during previous tests. Observe how deep learning algorithms are adapting to the installation and check for consistent analytical results.

Conclusion

Video surveillance analytics tools have transformed the way modern businesses approach security and building management. With smart solutions autonomously and continuously observing key areas, human teams can optimize the delivery of multiple essential services.

From enhanced threat detection and incident response capabilities to the generation of high-quality data insights, AI-powered video analytics technology has the potential to support teams across most major industries. With a good understanding of how these technologies work, and how to optimize their operation, effective and bespoke systems can be developed.