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

Can Video Analytics stop suicide?

Every year more than 1000 people around the world die inside of Metro railway stations.  The use of Video Analytics can help to identify and prevent suicide attempts. You might wonder how? The answer is very simple: falling detectors.

Falling detectors are used in public and private places for automatic detection of abnormal situations related to falling objects. It is important for high-quality security and reduces the numbers of severe accidents.

Tasks solved by falling detectors may be divided into two categories:

The detection of everything that falls from the platform and the detection of unintentional falls whilst on the platform (serious injuries and citizens living alone). In both cases immediate treatment is very critical, thus falls need to be detected as soon as possible.

In these two cases detectors work in different ways.

In the first case the detection logic is based on signal line intersection. This line is the boundary of the platform and the rails whereby the intersection of it is secured by an alarm signal. Hence the fall detector can automatically track all abnormal situations associated with falling of objects. It increases the speed of response and helps to decrease the number of death.

In the second case the evaluation of falls is more difficult as there is no dangerous boundaries and body movements and the fall is unpredictable. The principle of the fall detection in this case is based on a combination of motion gradients and human shaped features variation. Large movements always accompany falls, but large motion can also be a characteristic of walking person. That is why we need further analysis. An analysis of changes in the human shape. Especially width to height ratio up to a certain threshold is considered to detect a fall. And the last analysis is a luck of motion just a few seconds after the fall. All these steps help to identify falls and can trigger alarm signals in order to provide medical help quickly.

What are the main benefits of fall detection?

Video analytics helps timely response to the fact of people fall in the observed area and helps to providing quick treatment, which in most cases is critical. Therefore it helps to decrease the number of accidents and may save lives.

References:

http://www.iaeng.org/publication/WCECS2009/WCECS2009_pp1311-1316.pdf

http://synesis.ru/solutions/cepheus/detektor-padenia

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465367

Handsfree driving

The future of driving with the Google Self Driving Car. How video analytics will make your life easier.

Planning your next road trip? Imagine a world where you could just enter in your car, set up a destination, and then relax. This is almost real!

The first to imagine such car was Norman bel Geddes who designed General Motors’ Futurama exhibit for the ‘39 World’s Fair in New York’. Consider as a dream as this time, it is on the way of becoming real. Google’s project, which is led by Sebastian Thrun, former director of Stanford’s Artificial Intelligence Lab, is one of the most promising improvements for the car industry.

As Larry Burns, former GM research chief, professor at University of Michigan and a Google adviser said:  “Autonomy changes everything. Giving automobiles the ability to drive themselves is the biggest thing to happen to the automobile since the automobile".

How does it work?

Google self-driving car is an extension of the already existing driver aids that you can find in some luxury cars such as the BMW I3 or Mercedes.  The techniques used, are lane keeping systems, adaptive cruise control system, auto parking system, emergency braking and satellite navigation systems.  The driverless car is basically using all these systems together with the help of specifics software. The car covered with sensors and cameras is also able to keep track of the surrounding environment. Every element such as lane marking, reading signs, traffic lights and pedestrians are detected and analyzed with the help of cameras, radars, lidars (specific type of radar) and video analytics software.

Moreover in order to have an accurate positioning the Google self-driving car is using specific technology such as gyroscopes and accelerometers. It also scans its surroundings and collects as much data as possible, which are then analyzed in ordered to extract precious data in case of accidents.

What are the benefits of such innovations?

Such innovations will completely change the relationship we currently have with the automobile. It will also reduce drastically the number of car accidents, reduce the traffic flow and allow disable or old people to finally use their car. Also application such as Uber would be able to increase its efficiency and reduce its costs by spreading their driverless car fleet in important cities around the world.

What is the main issue?

However there is some concern related to this major innovation. The main challenge for autonomous vehicles is the way they communicate and interact with other cars. Several situations such as deciding who should proceed first around an obstruction needs a great deal of situation analysis and elaborated video analytics software.

To conclude video analytics is again used for a major innovation that will probably change our everyday life. Combined with other technology video analytics can really improved our daily life and object that are today consider common will maybe be perceived in a complete different way tomorrow.

References: 

http://www.economist.com/blogs/economist-explains/2013/04/economist-explains-how-self-driving-car-works-driverless

http://online.wsj.com/news/articles/SB10001424127887323808204579085271065923340