CCTV Brings In Adaptive Video Analytics To The Industry
The emerging use of CCTV cameras transcends from security purposes to creating a powerful security and business intelligence solution. CCTV cameras are now being utilized for reasons other than keeping an asset or a property safe.
We are tapping a greater potential of a CCTV camera coupled with fiber internet connection. Now it is embedded with self-learning video analytics technology that enables video surveillance camera to adjust to changing scene conditions and continually adapts without the need for manual calibration. A superior image capability integrated with powerful analytics performance. Below are common applications of adaptive video analytics in CCTV cameras. You can only imagine what it can do more in the future.
Congestion detection is used to spot a build-up of congested places in an area of interest especially in train stations, railways, parks, exit roads, retails queues and other public places. This pushes the leverage for timely actions and reactions. Also, this can give statistics when it comes to staff planning and marketing initiatives. For example, it can identify when a shopping area is at its busiest time of the day or year, or when a supermarket queues begins to get longer.
Video analytics motion detection is very useful in detecting unauthorized entries. Let’s say, a staff leaves by an unapproved exit. Certain areas of interest can be spotted automatically in a scene and searched through a recording. It identifies and scans any significant motion that happened during the recording. This comes useful when searching for motion in a steady and quiet place throughout a long period of a recorded video. It can be set using parameters like the size of an object and its sensitivity. A “no-motion” set-up, on the other hand, allows you to monitor things that should be moving and alert you when motion stops like escalators and conveyor belts to name a few.
Background modelling is vital to almost every kind of analytic behavior intended to be utilized in outdoor applications. It has the capability to identify elements in the background and can remove those traits from the scene. Because of this capability, it can perform an analysis of the remaining object within the field of vision.
Abandoned Object Detection
This video analytics abandoned object detection are implemented for alarm generation when an object has been left in a busy scene. It could be a package or a suitcase in an airport or in the subway. This key feature plays a major role in swift and timely management of threats and dangerous situations. On top of that, it can be used daily for illegal parking, or vehicles parked too long in certain zones. It’s also used to look for recordings of events such as parking violations and blocked highways.
Intrusion – Virtual Tripwire
A video analytics virtual tripwire is normally placed alongside a railway truck, motorway, area perimeter, or around a temporarily parked property. You can set up an alarm when the tripwire is breached based on the direction of the approach. The same manner can be done with a virtual tripwire. It can be placed with a monitored environment coupled with an alarm system that can only be triggered when both tripwires are crossed.
For your CCTV installation guide, you can actually set a face recognition to catch an act of theft of static objects. In this option, the video analytics sensitivity can be configured when moving foreground objects are ignored. This can also be utilized when reviewing a recorded footage especially in a warehouse, stockroom or in the retail industry. It can promptly detect when a targeted item was moved or taken out from a static location.
Counter Flow and Crowd Detection
Video analytics in counter flowing is made available to spot an individual or a vehicle moving in an unauthorized direction. Let’s say a car is traveling in the wrong direction in the street or it could be someone moving against flow of airport security. Counter flow analysis is a significant help when it comes to crowd control in public places, transportation terminals, highways, and busy streets.
Video analytics hooded camera mode notifies when a camera’s view has been obscured. Some common examples are cameras covered with a bag or a camera drenched in spray paint or being deliberately obscured.
The biometric facial recognition systems read faces of individuals from incoming video feed and compares these to a massive data of facial images. It then alerts when a positive match is detected. Facial recognition features include face detection, recording facial images, reads and matches faces with those in stored data, and an automatic procedure to search the closest match. Among the common application are VIP lists when staff are made aware of expected important guests; black lists when known offenders are detected for public warning; banking transactions when verifying the persons attempt at a financial transaction; access control verification when confirming identity; and mustering when keeping a tally of who is in and out of the premise.
How does video analytics work?
Most video analytic systems work in a series of processing steps. The first basic step is dissecting the content of what’s happening in the video—frame by frame. The video analytic systems can be breakdown on two key concepts.
By scanning and examining each pixel in the frame, the video analytics software is able to pick up even the slightest movement. The video motion detection or VMD combines the images fed by the CCTV camera with the software that analyzes the images as they are captured. However, there are specifications for optimal function of this feature. VMD relies heavily on two major components: the image quality from the CCTV camera and the quality of the analytics software in use.
There should be enough light and it’s prone to camera blinding if there’s too much backlighting. Even when using a sensor-activated light because of the shadows it can create. However, this doesn’t stop industries from using it as a widely implemented security solution. As a remedy, putting up a layer of analytics is a reasonably cost-effective.
Motion detection normally detects thermal heat or radar. Thermal cameras have built-in sensors that is capable of creating an image based on temperature differences between objects within the frame or field of vision. These kind of CCTV cameras pick up heat signatures like smoke or fog and are completely immune to different problematic visual conditions such as shadows, darkness, backlight, or camouflaged objects. As for the use of radar on motion detection, radio waves are transmitted and these same waves bounced off objects within the field of detection. This kind of CCTV camera uses technology that can calculate distance, velocity and size of objects. The good thing about this camera is less interference from daily obstructions that commonly cause false alarms. Radio waves easily passes through unsubstantial objects such as leaves, smoke, spider webs, hence, allowing radar to focus only on objects of significance.
In pattern recognition, objects are distinguished within a frame. Specific objects or patterns are being programmed for recognition and are set to be recognized within the frame. Once the patterns are analyzed and embedded into the program, the system then qualifies the said changes in each, correlates them over multiple frames, and finally, interprets these correlated changes. In the event that any changes happen such as when an object is moved, gone missing or a new item is added, the system immediately detects it and sends out an alert.
The Advantages of CCTV Video Analytics
Video analytics extends a lot of benefits to a new or existing CCTV system. IP video surveillance can often be tricky and time-consuming given that you have to keep track of everything that’s been going on 24/7. Video analytics lessens the burden that comes with round-the-clock surveillance. With such sophisticated algorithms and pixel by pixel analysis, it’s capable of detecting the smallest of details. Video analytics filters can be intelligently customized to meet specific security needs.
The Limitation of Video Analytics
The integration of video analytics in CCTV cameras for security purposes brings in great promise of better public and private safety. In fact, this is only the start of what it can do. Video analytics is evolving and adapting. However, as of recent, video analytics also poses a few downsides. The extent to which it can prevent and solve incidents can be prone to exaggeration. A common scenario would be when the authorities want to determine an unattended baggage in a busy public area at peak hours, the system can end up giving too many false alarms and, most likely, the reading cannot be fully relied upon. Another thing is a question of accuracy. License plate recognition has been implemented for quite some time already but it’s not a hundred percent accurate. Face recognition known to be difficult to perform reliably. Nonetheless, video analytics in CCTV cameras undeniably offers a better security surveillance performance. The one thing we should be worry about is the door it opens to the possibilities of abuse when implemented for other reasons other than safety and security.