Advertisment

Video Analytics: Physical Security goes to Next Level

author-image
PCQ Bureau
New Update

Over the past decade and especially after incidents like

“9/11” and "26/11", the emphasis on video based surveillance technologies has

really picked up. The use of IP surveillance cameras and CCTVs have increased

across both government and private enterprises. However, deploying these devices

alone isn't enough. The next challenge lies in the physical monitoring of videos

being grabbed by them. There's bound to be human error while monitoring

surveillance cameras 24x7. That's where video analytics technology comes to the

rescue. Let's understand this with the help of two scenarios--first without and

then with the use of video analytics:

Advertisment

Scenario 1: A terrorist scales the boundary wall of

a petroleum company's oil depot and plants bombs at the oil storage tanks, which

cause vast damage. The installed CCTV or IP surviellance cameras grab the

footage of this entire activity, which are later analyzed by the security

agencies to investigate further.

Scenario 2: A terrorist scales the boundary wall as

in the first case. This time, there are motion-detection surveillance cameras

installed, which detect the suspicious movement, and trigger an alarm to notify

the security personnel about the location of intrusion. The security people

reach the location on time to prevent the mishap from happening.

The above example very clearly illustrates the power of

video analytics solutions, clearly indicating that simple surveillance

techniques are just not enough today. You need video analytics solutions to make

sense of the video being grabbed and take necessary action 'in time'. They can

be used to provide real-time security, with an emphasis on preventing security

incidents instead of recording them for post analysis. Let's understand how it

works.

Advertisment

Video Analytics and its usage



Video Analytics is a way to do automated analysis of video content for

specific data, behavior, objects or any other user-defined events. The

technology is primarily used for physical security and business intelligence

apps. The video analytics software capability ranges from motion detection to

complex algorithms for detection of people, objects and their behavior. Similar

to human vision, which is perceptual and cognitive, video analytics also employ

complex vision algorithms to enable the solution to see and interpret from the

video content. The objective of video analytics is to understand the scene and

not just do motion detection. In case of motion detection cameras, even a single

pixel change can trigger an alarm, while systems with video analytics can ignore

motions from tree foliage, rain or snow, and can concentrate on scene change due

to human or any other object's motion.

Video Analytics softwares like CyeWeb from NovoSun that

takes the video feeds from IP surveillance cameras and analyzes them for

user-defined incidents

Video analytics can be deployed to automate security

surveillance, be it through CCTV cameras or IP surveillance cameras. For

instance, video feeds from cameras can be continuously analyzed to detect

presence of people and interpret their activities. So, suspicious activity or

people moving in unauthorized areas would automatically raise a flag and notify

the security personnel. In case of business intelligence apps, video analytics

can be used in retail stores to count number of customers waiting in a queue and

if the wait is getting longer then extra billing counters could be opened to

facilitate speedier customer service. These applications are already in

commercial use, both by enterprises and government agencies not only for

security purposes but also for better management.

Advertisment

Video Analytics techniques



Video analytics is the process of detecting changes that occur over

successive frames of video, correlating and qualifying these changes over

multiple frames and then finally interpreting these changes. These steps can be

broken into following processes:

Segmentation: This is the process of identifying the

changes and extracting those relevant changes for further analysis and

qualification. The pixels that have changed in successive video frames are

called as “Foreground Pixels” and those that are in same place are called as

“Background Pixels”. The degree of change is used to identify the foreground

pixels and results in one or more foreground blobs, where a blob is a collection

of connected pixels.

Classification: This is the process of qualifying

each blob and categorizing it. This results in broad categorization of each blob

into distinct identifiable classes as person, car, object, etc. This requires

recognition process to be incorporated into Classification stage. After

classification each blob in the single frame is identifiable to pre-defined

object and are selected in a manner such that they provide sufficient

discrimination between each classified blob.

Advertisment

Tracking: The classified foreground blobs take place

over a series of successive frames as the object moved through the scene. The

position of the blob is identified in the starting frame, and the trajectory of

the object can be calculated by connecting the position of the blob in

successive frames.

Activity Recognition: This is the final step where

the results of tacking and classification are combined to correlate multiple

blobs so as to infer the activity occurring in the video. For instance two

blobs, one corresponding to a car and another to a human are merging, then it

can be inferred that the person got inside the car.

Once the foreground objects have been segmented,

classified, tracked and their activities identified. Then the behavior of other

objects in the scene relative to the given object can be analyzed. For example

incidents like a person found loitering in the unauthorized area or a bag lying

unattended in the airport lobby would trigger the system to raise alarm and

notify the security personals.

Advertisment

Types of Video Analytics solutions



Video analytics extends beyond motion detection and incorporates many

advanced analytics capabilities like facial recognition, behavior recognition,

people counting and wide-area perimeter intrusion detection. Vendors are

offering software suites that cater to either of these capabilities or are a

complete suite of all these capabilities. Video analytics can either be done at

the server end or at the edge, i.e. within the camera. Whereas analytics being

done on the server end can be from third-part softwares like CyeWeb from NovoSun

that takes the video feeds from IP surveillance cameras and analyzes them for

user-defined incidents.

In-Camera Analytics: Embedding video analytics

technology on the edge devices like IP cameras dramatically reduces the

bandwidth and storage requirements by eliminating the need to send video feed

across the network to a centralized server for process of analysis and storage.

By analyzing the high-quality video at the point of capture increases the

accuracy of the alarms generated by analytics, thus reducing false positives and

increasing the belief of security personals on the alerts generated. By

embedding analytics within the camera, the edge devices can analyze video feed

in real-time as it is being captured, and when a rule is violated the

notification is sent instantly to security personals, thus decreasing the

response time as well. The organizations can easily deploy and scale the video

analytics embedded devices without the need to invest on additional servers for

video analysis and on storage.

Today the IP cameras themselves combine recent chip designs

that allow better compression technologies (such as H.264) to enable high

definition video transmission with less bandwidth. This in turn has resulted in

in-camera analytics for large scale surveillance systems using megapixel

cameras. Further, advanced digital signal processing technologies and hardware

accelerators do the heavy encoding/decoding of the video to handle intense

processing requirements of video analytics within the edge device (IP camera).

Server-End Analytics: Embedded analytics in cameras

is ideal for situation where /2010/images/video is coming from one particular camera.

But when video feeds are coming from multiple cameras, as in a shopping mall,

then to perform video analytics you need higher processing power and central

management capabilities of a centralized server-based solution. Such solutions

enable more complex analytics and quicker searches of the video archives. Video

analytics that require facial recognition or object recognition or any archive

search based on a particular image match are beyond the realm of in-camera

analytics solutions. In large retail outlets, a centralized server based video

analytics solution would be beneficial since it can be used for counting people

who have entered the outlet, if any customer has not stolen any object off the

shelf, by counting people in queues it can be used for better crowd management

as well, because the video feeds would be coming from several cameras spread

across the outlet and would notify the security manager based on incidents

collected from collection of cameras and not just one particular camera.

Thus, video analytics enables security personnel to prevent

a crime from happening instead of investigating it after the incident. It also

minimizes the operational cost of a distributed video surveillance solution by

allowing real-time centralized administration and monitoring.

Advertisment