Police and security teams guarding airports, docks and border crossings from terrorist attack or illegal entry need to know immediately when someone enters a prohibited area, and who they are. A network of surveillance cameras is typically used to monitor these at-risk locations 24 hours a day, but these can generate too many images for human eyes to analyze.
Now, a system being developed by Christopher Amato, a postdoc at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), can perform this analysis more accurately and in a fraction of the time it would take a human camera operator. "You can't have a person staring at every single screen, and even if you did the person might not know exactly what to look for," Amato says. "For example, a person is not going to be very good at searching through pages and pages of faces to try to match [an intruder] with a known criminal or terrorist."
Existing computer-vision systems designed to carry out this task automatically tend to be fairly slow, Amato says. "Sometimes it's important to come up with an alarm immediately, even if you are not yet positive exactly what it is happening," he says. "If something bad is going on, you want to know about it as soon as possible."
So Amato and his University of Minnesota colleagues Komal Kapoor, Nisheeth Srivastava and Paul Schrater are developing a system that uses mathematics to reach a compromise between accuracy — so the system does not trigger an alarm every time a cat walks in front of the camera, for example — with the speed needed to allow security staff to act on an intrusion as quickly as possible.
For camera-based surveillance systems, operators typically have a range of computer-vision algorithms they could use to analyze the video feed. These include skin detection algorithms that can identify a person in an image, or background detection systems that detect unusual objects, or when something is moving through the scene.
To decide which of these algorithms to use in a given situation, Amato's system first carries out a learning phase, in which it assesses how each piece of software works in the type of setting in which it is being applied, such as an airport. To do this, it runs each of the algorithms on the scene, to determine how long it takes to perform an analysis, and how certain it is of the answer it comes up with. It then adds this information to its mathematical framework, known as a partially observable Markov decision process (POMDP).
Then, for any given situation — if it wants to know if an intruder has entered the scene, for example — the system can decide which of the available algorithms to run on the image, and in which sequence, to give it the most information in the least amount of time. "We plug all of the things we have learned into the POMDP framework, and it comes up with a policy that might tell you to start out with a skin analysis, for example, and then depending what you find out you might run an analysis to try to figure out who the person is, or use a tracking system to figure out where they are [in each frame]," Amato says. "And you continue doing this until the framework tells you to stop, essentially, when it is confident enough in its analysis to say there is a known terrorist here, for example, or that nothing is going on at all."
Like a human detective, the system can also take context into account when analyzing a set of images, Amato says. So for instance, if the system is being used at an airport, it could be programmed to identify and track particular people of interest, and to recognize objects that are strange or in unusual locations, he says. It could also be programmed to sound an alarm whenever there are any objects or people in the scene, when there are too many objects, or if the objects are moving in ways that give cause for concern.
In addition to port and airport security, the system could monitor video information obtained by a fleet of unmanned aircraft, Amato says. It could also be used to analyze data from weather-monitoring sensors to determine where tornados are likely to appear, or information from water samples taken by autonomous underwater vehicles, he says. The system would determine how to obtain the information it needs in the least amount of time and with the fewest possible sensors.
Amato and his colleagues will present their system in a paper at the 24th IAAI Conference on Artificial Intelligence in Toronto in July.
Written by Helen Knight, MIT News Office
Caroline McCall | EurekAlert!
Cloud technology: Dynamic certificates make cloud service providers more secure
15.01.2018 | Technische Universität München
New discovery could improve brain-like memory and computing
10.01.2018 | University of Minnesota
On the way to an intelligent laboratory, physicists from Innsbruck and Vienna present an artificial agent that autonomously designs quantum experiments. In initial experiments, the system has independently (re)discovered experimental techniques that are nowadays standard in modern quantum optical laboratories. This shows how machines could play a more creative role in research in the future.
We carry smartphones in our pockets, the streets are dotted with semi-autonomous cars, but in the research laboratory experiments are still being designed by...
What enables electrons to be transferred swiftly, for example during photosynthesis? An interdisciplinary team of researchers has worked out the details of how...
For the first time, scientists have precisely measured the effective electrical charge of a single molecule in solution. This fundamental insight of an SNSF Professor could also pave the way for future medical diagnostics.
Electrical charge is one of the key properties that allows molecules to interact. Life itself depends on this phenomenon: many biological processes involve...
At the JEC World Composite Show in Paris in March 2018, the Fraunhofer Institute for Laser Technology ILT will be focusing on the latest trends and innovations in laser machining of composites. Among other things, researchers at the booth shared with the Aachen Center for Integrative Lightweight Production (AZL) will demonstrate how lasers can be used for joining, structuring, cutting and drilling composite materials.
No other industry has attracted as much public attention to composite materials as the automotive industry, which along with the aerospace industry is a driver...
Scientists at Tokyo Institute of Technology (Tokyo Tech) and Tohoku University have developed high-quality GFO epitaxial films and systematically investigated their ferroelectric and ferromagnetic properties. They also demonstrated the room-temperature magnetocapacitance effects of these GFO thin films.
Multiferroic materials show magnetically driven ferroelectricity. They are attracting increasing attention because of their fascinating properties such as...
08.01.2018 | Event News
11.12.2017 | Event News
08.12.2017 | Event News
22.01.2018 | Materials Sciences
22.01.2018 | Earth Sciences
22.01.2018 | Life Sciences