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Zensors introduces a camera based deep learning model for tracking cars

Zensors is a Carnegie Mellon spinout and maker of cloud-based visual sensing technology. The company is enabling smart and reactive spaces through cutting-edge computer vision technologies.

It has now released its latest deep learning technology, Car Pose Net that enables tracking of 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 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.

The company sees it as the evolution of what is possible with camera-based sensing that presents the potential to maximise the camera infrastructure to generate new data streams.

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 Cloud, or accessed via CSV or API for integration into other systems.

Source: Press Release, Zensors

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