By a Staff Writer
Pittsburgh, June 18, 2019 – Today, Zensors, a Carnegie Mellon spinout and maker of cloud-based visual sensing technology announced the release of its latest deep-learning technology entitled Car Pose Net.
Previously, tracking rigid, three dimensional objects like cars using only single-view cameras was problematic. Car Pose Net fits three-dimensional (3D) pose wireframes to cars, improving tracking results, especially in difficult conditions like snow or partial visual obstructions.
In a news release Zensors points out: “This unlocks incredible potential for existing city and autonomous vehicle camera systems. Because the technology can be deployed using legacy camera hardware, with Zensors’ edge on cloud compute platforms, more advanced, accurate, and real time traffic data can be unlocked.”
Anurag Jain, product development head at Zensors
“It’s really the evolution of what is possible with camera-based sensing,” said Anuraag Jain, head of product development at Zensors. “The potential to maximize the camera infrastructure that a city already owns to generate new data streams is really exciting.”
Enabling ‘congestion pricing’ and easier traffic violation enforcement
Jain said that the most interesting use cases for this type of deep learning were in traffic management and “congestion pricing” which is coming to New York City as early as 2021. Other potential applications for Car Pose Net include traffic violation enforcement, including wrong-way and double-parking detection.
Car Pose Net is integrated into the Zensors platform, and allows city managers to make more data-driven decisions. Camera footage is passed through the deep learning model and turned into statistical data, which can be viewed in charts or real-time dashboards in the Zensors Cloud
It can also be accessed via comma-separated value or CSV format, or application programming interface or API, for integration into other systems.
“Because we’re able to work off of existing infrastructure, our time to deploy is days or weeks, rather than months or even years needed to blanket a city in new sensors,” said Jain.
He added: “This makes the capital investment needed to deploy significantly less than other, more hardware dependent tracking systems.”
Zensors will be exhibiting its Car Pose Net technology from June 18 to June 20 at the Computer Vision Pattern Recognition (CVPR) Conference in Long Beach, California.
About Zensors:
Spun out of Carnegie Mellon University, the birthplace of Artificial Intelligence, Zensors wants to enable smart and reactive spaces through cutting-edge computer vision technologies. We believe that advances in AI should be accessible to everyone, not just those with a degree in computer science, and applied to everyday problems to make experiences more delightful and cities more efficient.
