- Video Analytics: Business Intelligence + Security
Take the international clothing retailer Benetton, which is using video analytics to glean intelligence about customer shopping habits, potentially lucrative information. Yet when enterprises do discuss the strategic benefits of analytics beyond security uses, they often come up in the second round of talking points. This is unfortunate because that strategic focus, the market is getting bogged down in a debate about architecture, particularly whether edge-based or server-based analytics are superior. More in
4. The Rise of Video Analytics
By Josh Manion
Chief Executive Officer
Stratigent, LLC
According to eMarketer, online video advertisements grew 89% from 2006 to 2007, and are projected to grow 68% from 2007 to 2008 to $1.3 billion dollars. By 2010, online video advertising is expected to reach almost $3 billion. And by 2011, almost 90% of the U.S. internet population will have consumed online video.
What is behind the rise of online video?
Clearly, the acquisition of YouTube by Google broadened the reach of online video and encouraged active participation by users. The availability of news, movies and TV shows online is also driving the growth of online video. NBC expects 2,200 hours of Olympic events to be online and available for viewing. Also, the social engagement of easily sharing videos with others is driving growth.
Making video analytics actionable
Despite this significant growth in the adoption of online video, effectively measuring and analyzing the usage of online video is still in its infancy. Many issues must be overcome including technical challenges to the many different video player formats currently being used, uncertainly about the most useful KPI’s, and the overall lack of talented analytics resources that have experience with video analytics.
In order to start taking action on video analytics it is helpful to first outline a high-level grouping of the types of metrics (or video analytics KPI’s) that I have found to be of interest when it comes to measuring video analytics.
- Basic Metrics: This includes views, visits, unique viewers, duration of views, and the technical profiles of the visitors, etc.
- Engagement: Which videos have the highest visitor engagement, where do visitors abandon the video, and which video segments capture their attention and cause them to rewind, etc. Applying this type of data to the specific video asset in question is often a key requirement.
- Distribution: Distribution helps to understand where video’s are consumed, URL’s they are being viewed at, the type of referral source, (e.g. email, RSS, etc) and geography of the viewers consuming content.
The next step in the process is to select an appropriate set of technology to collect and analyze video analytics data. Several tools provide varying degrees of video measurement. Traditional web analytics vendors like Omniture and WebTrends both released video analytics functionality this year. Another option is to work with firms that specialize in video analytics, such as Visible Measures. In each case, you will have the capability to track the basic metrics, but in the case of the more specialized firms you will have access to additional capabilities around tracking distribution, engagement and the performance of individual video assets within the context of the video itself. As always, when considering this type of investment, it is important to consider the level of effort required for the implementation, which is often dependant on the number and type of video players your organization uses. Additionally, I encourage you to consider the level of data integration needed with your other marketing analytics data so you can effectively evaluate the total investment necessary to meet your business requirements.
For more information please contact 877-427-8900 or email info@stratigent.com.
5. Top 3 Problems Limiting the Use and Growth of Video Analytics
http://ipvideomarket.info/report/top_3_problems_limiting_the_use_and_growth_of_video_analytics
by John Honovich, IP Video Market Info posted on Jun 19, 2008
While video analytics holds great promise, people are still asking about the viability of using analytics in the real world. Indeed, as stories of video analytic problems have spread, concerns about the risks of video analytics now seem higher than a few years ago when the novelty of the technology spurred wide excitement.
This article surveys the main problems limiting the use and growth of video analytics. It is meant to help security managers and integrators gain a better sense of the core issues involved.
Top 3 Problems:
- Eliminating False Alerts
- System Maintenance Too Difficult
- Cost of System Too High
Eliminating False Alerts
Since the goal of video analytics is to eliminate human involvement, eliminating false alerts is necessary to accomplish this. Each false alerts not only requires a human assessment, it increases emotional and organizational frustration with the system.
Most are familiar with burglar alarm false alarms and the frustration these causes. On average, burglar alarm false alarm per house or business are fairly rare. If you have 1 or 2 per month, that is fairly high. Many people do not experience false alarms of their burglar system for months.
By contrast, many video analytic systems can generate dozens of false alarms per day. This creates a far greater issue than anything one is accustomed to with burglar alarms. Plus, with such alarms happening many times throughout the day, it can become an operational burden.
Now, not all video analytics systems generate lots of false alarms but many do. These issues have been the number one issue limitation of the integrators and end-users that I know using and trying video analytics.
System Maintenance Too Difficult
System maintenance is a often overlooked and somewhat hidden issue in video analytics.
Over a period of weeks or months, a video analytic system's false alerts can start rising considerably due to changes in the environment, weather and the position of the sun. This can suddenly and surprisingly cause major problems with the system.
Not only is the increase in false alerts a problem, the risk now that the system could unexpectedly break in the future creates a significant problem in trust. If your perimeter surveillance one day stops functioning properly, you now have a serious flaw in your overall security plan.
This has been a cause of a number of video analytic system failures. The systems, already purchased, simply get put to the side becoming a very expensive testament to not buying or referring one's colleagues to video analytics.
This being said, not all video analytic systems exhibit this behavior but you would be prudent to carefully check references to verify that existing systems have been operating for a long period of time without any major degradation.
Cost of System Too High
While you can find inexpensive video analytic systems today, these system tend to exhibit problems 1 and 2, high false alerts and poor system maintenance. Indeed, in my experience, video analytic systems that are either free or only cost $100-$200 more generally have significant operational problems.
One common feature of systems that work is that the complete price for hardware and software is usually $500 or more per channel for the analytics. Now just because a video analytic systems is expensive obviously does not mean it is good. However, there are necessary costs in building a systems that is robust and works well in the real world.
The cost of video analytic systems comes in making them robust to real world conditions that we all take for granted. The developer needs to make the video analytic system “intelligent” enough to handle differences in lighting, depth, position of the sun, weather, etc. Doing this involves building more complex or sophisticated programs. Such programs almost always require significantly more computing hardware to execute and significant more capital investment in writing, testing and optimizing the program. All of these clearly increase costs.
The challenge is that it is basically impossible to see this from marketing demonstrations because from a demo all systems invariably look exactly alike. This of course has the vicious effect of encouraging people to choose cheaper systems that are more likely to generate high false alerts and be unmaintainable.
If you select a system that works, the cost per camera can make it difficult to justify the expense. Indeed, so much of the first generation video analytic deployments, came from government grant money, essentially making the cost secondary or not relevant. Nevertheless, for video analytics to grow in the private sector, they will not only need to work they will need to generate financial return.
When video analytics allow for guard reduction or reduce high value frequent losses, it is easy to justify and you see companies having success here (in terms of publicly documented cases, IoImage is the leader here). For other cases, where humans are not being eliminated, the individual loss is small or the occurrence of loss is low, the cost can be a major barrier.
Conclusion
Though I anticipate video analytics successes to increase, I believe such success will be constrained to applications where the loss characteristics and/or the human reduction costs are high. While analytics will certainly become cheaper, such cost decreases will take time and in the interim, it is these high value applications where analytics can gain a foothold of success.
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