Pittsburgh International Airport and Manchester-Boston Regional Airport are amongst the first airports that are relying on Artificial Intelligence (AI) to reduce the stress of the holiday rush. Developed by Zensors, a Carnegie Mellon University startup, airports can provide travelers with real-time wait estimates at Transportation Security Administration (TSA) checkpoints to give passengers an idea of how much time they can expect to wait in the security queue. Passengers can access security wait times on airport websites before they leave home and allowing them to manage their time and diffuse "will I miss my flight" worries. "We know that the airport security screening process is very stressful for passengers and a significant operational challenge for airports and air carriers," said Anuraag Jain, founder of Zensors. "By using Artificial Intelligence to provide real-time data, airports can improve the passenger experience and optimize operations. Having enough time for a beer or coffee once airside is a huge relief for weary holiday passengers."

With Thanksgiving and Christmas just around the corner, Airports are turning to AI companies like Zensors to help relieve passenger's travel stress Air travel, whether for business or pleasure has become a regular part of millions of Americans' lives and so has the hassle and stress that flying through crowded US airports poses to travelers. From the mad dash for parking at the airport to the long lines at security checkpoints to navigating long crowded airport concourses air travel is not for the faint of heart. Despite nearly a trillion dollars expected to be spent on airport construction and expansion projects over the next 10 years, things are going to get worse before they get better as the numbers of flights and passengers continue to increase, further straining airports' capacity. More than 2.7 million passengers fly every day and airports are likely to see up to a 25% increase in passenger volume from late November through early January. Some airports are taking steps to help manage stressful holiday travel.

It may not help you get through the airport faster, but artificial intelligence (AI) can now give you information on wait times at Pittsburgh International Airport security checkpoints in real time. The airport recently announced a partnership with Zensors, a Pittsburgh-based company that applies AI to feeds from airport security cameras to estimate wait times at the airport's three Transportation Security Administration (TSA) checkpoints on a minute-by-minute basis. Wait-time information is then posted on the airport's information screens and website, including predictions about whether wait times will increase or decrease. Zensors' AI observes passenger volume and also includes factors like time of day and TSA staffing levels to make its estimates. "We know security can be a frustration for travellers and having accurate wait estimates can help set expectations and aid in planning trips," said Pittsburgh International Airport CEO Christina Cassotis.

Today, Zensors, a Carnegie Mellon spinout and maker of cloud-based visual sensing technology is announcing the release of its latest deep learning technology, Car Pose Net. Previously, tracking rigid, three dimensional objects (like cars) using only single-view cameras was problematic. Car Pose Net fits 3D pose wireframes to cars, improving tracking results, especially in difficult conditions like snow or partial visual obstructions. This unlocks incredible potential for existing city and autonomous vehicle camera systems. Because the technology can be deployed using legacy camera hardware and Zensors edge or cloud compute platforms, more advanced, accurate, and real time traffic data can be unlocked.

Computer vision is quickly advancing, but it tends to trickle into the world in scattered, specific applications. We encounter it when Facebook automatically tags a friend in a photo, or when Google suggests images similar to one we're searching for. But the real promise is much more exciting. A camera, properly trained, could answer simple, human questions like: "Are my kids home from school?" or "Is there a parking spot open at work?" or "How many people are in line at Shake Shack?" In other words, computer vision could make our homes and our cities smart.

That clunky old Android phone sitting in a drawer may be more useful than you think. A team from Carnegie Mellon University has created a program called Zensors that uses connected smartphone or surveillance cameras to track your environment, figure out what's going on and give you valuable alerts and statistics. The team showed how a user can point a smartphone outside a window, circle an area of interest, and pose a natural-language question like "how many cars are in the parking lot?" Zensor then proceeds to track cars as they enter and leave, giving a business data about its customers. Such tasks are way beyond the means of regular smart home cameras and sensors, but the tech isn't quite as magical as it sounds. The researchers are relying on crowd-sourced workers to for complex tasks like counting cars, while easier tasks are calculated by algorithms.