Following decades of slow market penetration and setbacks, the Intelligent Video Surveillance, ISR Analytics and Video Analytics industry is forecasted to experience decades of rapid growth. In the past year, intelligent video surveillance, ISR and video analytics with mature technology have attracted a lot of attention. The technology, which automatically identifies people and objects in video content, has matured to the point of practicality and is now winning business adoption, according to another report from technology research firm ABI.

The Intelligent Video Surveillance, ISR & Video Analytics: Technologies & Global Market – 2013-2020 report indicates that the global Intelligent Video Surveillance (IVS), Intelligence, Surveillance & Reconnaissance (ISR) and Video Analytics (VA) industry revenues* totalled $13.5 billion in 2012, and are expected to grow at 13.8% CAGR from 2012 to 2020.

Features like motion detection and camera tamper, embedded in surveillance devices and offered as free by manufacturers, will be increasingly joined by applications for more sophisticated video content analysis (VCA). There are also technology improvements such as (fewer) false alarms, searchable analytics and increasing processing power.

Examples of applications of video analytics include recognising human faces in speeding cars, people counting, identifying customer traffic patterns, assessing customer dwell times at different areas of a shop, analysing the length of queue lines etc.

The rapid market growth is driven by:Technology maturity, Increased use of video surveillance due to high processing rate of video surveillance compared to human operators who entail high cost and have a high rate of overlooked events, Migration from analog to digital and IP-based cameras, Cost reduction of video analytic systems and Improved cost-performance of new edge-based video analytics DSP technologies.


Sources:

http://www.information-age.com

http://www.securitysystemsnews.com

http://www.prweb.com

http://www.rnrmarketresearch.com

http://www.homelandsecurityresearch.com

Video analytics in retail another point of view.

Not that long ago, it wasn’t uncommon for retail outlets to have surveillance cameras stacked up in their corners to monitor customer movement with the primary purpose of preventing theft and catching shop lifters. Nowadays, retailers have discovered that this practice of monitoring customer movement, known as video analytics can also be used to serve as business process monitors, providing valuable data and insights that offer real benefit to the operations and people they serve.

Today, video analytics systems have become more sophisticated and technologically advanced and can significantly empower retailers to maximize efficiency whilst monitoring customer traffic and in-store customer behaviour, improving operational efficiency, minimizing theft and loss, increasing customer safety and ultimately improving sales and increasing profit.

By expanding the profile of the cameras, consumer researchers can analyse and push out dashboard reports on consumer behaviour. Businesses can better understand market behaviour in stores, what drives a person to a specific spot in the store, who is busy in the store at specific times of the day, age group trends, what is the consumer buying, the time they spent in a specific area of the shop etc.


How does it work?

With advanced motion detection and pattern recognition, Video analytics software examines each pixel in the frame and picks up even the slightest movement. Specific patterns can also be programmed for recognition.

With Video Analytics Retailers can:

• Use heat maps from advanced video equipment to ascertain the areas with the greatest frequency and activity (“hot zones”)

•  Analyze store traffic to identify the dominant traffic paths

• Assess the effectiveness of window and marketing displays by measuring customer dwell time

• Use queue data to enhance operational efficiency and increase customer satisfaction

• Use surveillance networks to prevent accidents and enhance safety measures, detect suspicious in-store activity and monitor suspicious after-hours activity

Benefits

• Helps to pinpoint premium product positioning, determine general strategies for product placement and make better merchandising decisions
• Informs marketing decisions such as the most appropriate locations for in-store promotional campaigns
• Indicates the success of product placement and ROI on advertising investment
• Makes decisions that optimize staff allocation, space utilization and traffic flow in line with data regarding dwell time (stickiness) in certain areas 
• Understands the customer traffic at check-out points and product counters across different time periods and seasons, to more strategically allocate human resources across the store and optimize staff shifts
• Detects suspicious activities from customers and even employees

The ability to accurately see where and what people are doing throughout the mall gives management a greater capacity to cater for customer and tenant needs

Sources:

http://www.socialnomics.net

http://www.agentvi.com

http://blog.nrf.com

http://www.youtube.com

Cameras CAN read EMOTIONS…

or at least they will be able to soon enough. This is what researchers from NYU are trying to accomplish and, by the looks of it, they might be able to pull it off. Well to be fair, it will not be the camera itself that will be capable of such a feat; it is software that will be installed within the camera and that has been under development under the watchful eye of sponsors such as the Pentagon.

Now you might ask, “What is the point of that?” Well, the US Military thinks that developing software that can read emotions will play a key role in avoiding tragedies like the Boston Marathon Bombing. In fact, although today’s video analytics software can determine if a person is concealing a weapon or identify suspicious behavioral patterns, it is only able to do so after the fact.

However, imagine if a camera could detect suspicious behavior live and send out the appropriate alert or warning. This is what the team of researchers is currently working now and, according to them, they can already identify positive emotions and next on their list are negative/aggressive emotions. This has been useful mainly for marketing purposes, in order to detect the type of responses crowds come-up with when exposed to different stimuli.

Nonetheless, although the theory seems to be relatively straightforward, such software will have to be ‘trained’. What is meant by that is that it will have to be loaded with data form people paid to analyze and tag video footage based on what they are shown on their screens. Researchers then take this data and upload it into a computer neural network that analyzes the relationship between the data, thus allowing the software to do so on its own.

Lets hope these guys can pull it off because, from what I have read, it seems that such technology will really be able to prevent or at least mitigate the impact of such events in the future.

References:

http://blogs.scientificamerican.com/observations/2013/04/18/httpblogs-scientificamerican-comobservationswp-adminpost-new-phppost_typepost/