by Bob Cutting

ISC West asked ObjectVideo to participate in a panel discussion on the topic of where video analytics will be in the next 10 years.  Past events hosting such a discussion have yielded a somewhat “same old, same old” outcome.  But I thought the discussion at ISC resulted in a much more productive outlook to where video analytics is heading with respect to analytics technology and real world solutions for end users. 

 My key takeaway was a very consistent message from the panel on the age-old question of “where analytics?”—referring to whether we will see more analytics on the edge or on a server.  We all answered in our own way, but as I was listening to all the responses, I realized we were all saying the same thing.  What finally came out is that the question is typically only answered half-way.  People focus more on real-time video processing for event generation.  But there’s a growing solution focus on using those events or underlying metadata in more advanced ways, either for forensic searching or for correlation to other sensor outputs and data streams (POS, ATM, access control…)

So while it’s hard to argue against using edge devices with embedded analytics for video analytic processing and real-time event and data generation, there is a growing solution based on leveraging that data across multiple cameras and locations for advanced business analysis and innovative detection scenarios using multiple data inputs that require back-end, server-based systems to manage the data correlation and detection/search policies.  We’re seeing this exact trend in the market, and these future solutions further justify the value that can be extracted from video analytic technology.

by David McGuinness

                    brave souls weather DC                                   FL: calm & balm

As I look out my window at the snow pack of Washington DC, I think longingly of balmy South Florida and the TechSec conference held earlier this month. For those of you who haven’t attended TechSec Solutions in the past, it is wholly dedicated to IP and network based security and it attracts manufacturers, integrators and consultants alike.  

There is a heavy focus on educational presentations and panels which is both necessary and important to help drive the transition to an IP world within the security and surveillance market.  Could 2010 be the year that IP solutions start to pick-up the pace of adoption?  Amongst a savvy group of attendees, the expectation is “yes” due to new (and imminent) product releases, lessons learned and standards initiatives.

So why is this important to ObjectVideo?  We understand that a technology shift to IP has been a gating factor to embracing new technology (i.e. video analytics) in the space.  That’s been the case dating back to 2003 when I started at ObjectVideo, and it’s still true.  But we’re starting to see a shift in technology adoption rates due to innovation, improvements and benefits afforded by IP offerings throughout the video ecosystem, and that’s laying the groundwork for an interesting 2010.

Improved performance + compelling new capabilities + lowered TCO = a persuasive argument for IP and advanced technology adoption.

by Gary Myers

Since my post last week, I’ve received a number of questions and comments so I thought I’d address them as a post (hopefully for everyone’s benefit.)

As Steve Mitchell points out, the end-user definitely has an impact on the performance of the analytics and training is needed.  He drew the analogy last week about pilots and airplanes.  Stretching this pilot/plane analogy a bit, the end-users are the pilots and OV is a part of the airplane (with the whole plane being the end product delivered to market by our partners.)   OV builds a component that works in many different planes and our responsibility is to make sure it performs in a wide variety of settings.  Our OEM partners deliver the complete plane, which includes working with the pilots (users) to understand how to operate the plane (product) most effectively.

As part of the release process, we qualify our software in several ways:

• Science testing to validate the newest release is at least as good, if not better, than prior releases. These automated tests utilize thousands of hours of videos and corresponding rules to approximate real-world scenarios. These are compared against the baseline results taking into account the metrics listed in my post.

• Product testing to ensure that the whole product works end-to-end, including manual testing to approximate the end-user experience.

ObjectVideo focuses on testing our software for release to our partners. Different partners focus on different areas so our partners are in the best position to provide the performance criteria to the end market based upon their own test methodologies, results and sales programs. In this way, they can effectively support their analytics-enabled products and know, as well, that those products are meeting the needs of their customers.

by Gary Myers

Since OV is the leader in the industry, we get asked a lot about analytics performance.  This can be hard to quantify as there are a lot of contributing factors. In general, accurate event detection is affected by some combination of camera angle, camera placement, lighting conditions, other environmental factors and system configuration. The goal when deploying and configuring an analytics-enabled system is to strike the proper balance between being too sensitive (causing false events) and not sensitive enough (causing missed events).

Over the years of building and testing our software, we’ve focused on three primary testing criteria when determining performance metrics: number of detected events, false events and missed events. The ideal case is to detect all expected events but have low numbers of false and missed events. If you catch all the expected events but you still have a lot of false ones, we would consider performance low as there will be too many nuisance events.  Likewise with the missed events – miss too many then overall user confidence goes down.

In future posts, I’ll cover some ways to improve effectiveness, either through camera setup or system adjustments, to enable the user to get the most from their investment in analytics.