July 23, 2019

Introducing Car Pose Net: A Camera-Based Deep Learning Model for Tracking Cars in Three Dimensions

On June 17th, Zensors, announced the release of its latest deep learning technology - Car Pose Net - at CVPR Conference in Long Beach CA, unlocking incredible potential for existing city and autonomous vehicle camera systems.

Introducing Car Pose Net: A Camera-Based Deep Learning Model for Tracking Cars in Three Dimensions

On June 17th, Zensors, a Carnegie Mellon spinout and maker of cloud-based visual sensing technology announced the release of its latest deep learning technology - Car Pose Net - at CVPR Conference in Long Beach CA. 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.

“It’s really the evolution of what is possible with camera-based sensing,” said Anuraag Jain, Head of Product at Zensors. “The potential to maximise the camera infrastructure that a city already owns to generate new data streams is really exciting.” 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, or accessed via CSV 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. “This makes the capital investment needed to deploy significantly less than other, more hardware dependent tracking systems.”

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