ML “Machine Learning”
Although we are still likely many decades away from anything considered close to “true AI”, many cameras and analytics systems are marketed and sold as being AI-driven or otherwise enriched through machine learning. While some of this is undoubtedly just marketing, there is truth to the notion that data analytics is making an impact in the field of video surveillance.
The ideas behind machine learning reach back into the earliest days of computer science, but only recently (within the last 5-10 years) has both the volume of data and processing power necessary become affordable enough to start making real strides. There are of course the famous industry examples like Google’s DeepMind and IBM’s Watson, but even without access to supercomputer hardware like that there are still many useful things that can be accomplished with a strong, stable connection to the cloud and a quality camera to create good data for analytics to process.
As they employ a subset of Artificial Intelligence, it would be more accurate to refer to these “smart” devices as Machine Learning, (ML) devices, that is if ever the average consumer becomes weary of the term AI being thrown around every industry from phone apps to kitchen appliances.
The way this type of technology works is through building a mathematical model using sample data, sometimes referred to as “training data”. This model is used to make predictions or decisions without the need for a programmer to directly intervene in the process. Machine learning is particularly important in the field of “Computer Vision”, the science and technology behind a computer’s processing and analysis of images and video. Because of this, ML has come to reflect a considerable portion of industry leading video surveillance and analytics systems. Historically it has been impossible to use conventional algorithms to perform such a task effectively.
Instead of trying to answer the question “Can we build machines that think?”, ML proposes that we try to “build machines that do what we (as thinking entities) do”.
Here we will take a look at the effect of this emerging technology and its practical application in video surveillance and analytics.
Let’s take a look at what exactly is possible using intelligent video. Smart IP cameras are able to classify the objects they see on their own. Objects entering or leaving the scene can be identified as a person, car, bike, truck, or other object. Because the camera can differentiate objects, it can be told to only alert in the case of a break-in, ignoring things like leaves in the wind or animals wondering through. New low-light cameras allow color-filtering even in scenes with very little ambient light. Even at night, color detection is possible in this way.
Alarm detection can be set to be even more specialized. Rules can be configured to look for specialized behavior patterns such as fighting, running, loitering, path following, abandoned luggage, entering an area, and more. The alarm engine in each camera coordinates with the others in a logical way to interpret this information and determine threat status. All this allows for a very robust alarm condition solution and prevents false and missed alarms.
What’s more, stored metadata enables forensic analysis at a later time. This means that retroactive searches for a specific car or person is possible even if it was not a determined item of interest until well after the event was recorded. Metadata is compact and only barely adds to the size of recordings. It is quick and easy to search through to find a specific event.
The logical next step is to continue to improve analytics for video metadata until we approach 100 percent practical use. Predictive analysis of human traffic patterns can predict shoplifting and identify potential events before they take place, and the more data that can be made useful the more accurate these types of predictions will be.
The same technology can monitor customer dwell time at different displays in a store and determine the effectiveness of in-store advertising and product locations. Analyzing customer engagement with these displays can help increase customer engagement with products and lead to increased sales and revenue. As the IoT expands, this type of technology will be more and more critical as there will be many more points of data to analyze. There is no way to fully anticipate the eventual effects this will have on our industry or the world at large.
Even though huge volumes of video data are collected every day, most statistics indicate that only 10 percent of this data is ever used. The majority of data collected loses its value very quickly after being generated. The reason for this? Our primary focus tends to be delivering the correct information in a crisis or providing it as evidence after criminal activity has taken place. This causes much data to be “wasted” in the sense that we lose out on the opportunity to perform useful analytics.
Video analytics is an increasingly powerful tool. It helps to improve usability of these vast amounts of video information. Analytics software acts as the “brain” of a surveillance system and is built into IP cameras themselves or processed in separate computing infrastructure. This creates a smarter system that “knows” what it sees and alerts guards to potential threats as soon as an alarm rule or condition is met. Analytics gives operators the chance to act faster and more efficiently with better intel.
ML Analytics is like an ever-vigilant system operator within the security system itself. Content analysis information, a form of video metadata, is stored as well. As they reduce operating costs and increase efficiency, intelligent cameras deliver a solid return on investment which can be measured in tangible results to the business or other setting in which it operates.
Security Technology of South Texas is happy to offer custom access control and surveillance solutions with video analytics to the greater South Texas area, designed either turn-key and from the ground up, or integrated into an already existing series of cameras.
Please contact us through email at email@example.com on our website or via phone at 210-446-4863 24/7 to schedule a consultation.