IISc. researchers develop software platform for ‘smart’ video tracking

The platform can be used to track an object across a 1,000-camera network.   | Photo Credit: Sudhakara Jain

Machine learning models scour through feeds from thousands of cameras set up in many cities across the world for specific purposes, such as tracking a stolen car. These models cannot work by themselves, and have to run on a software platform. But existing platforms usually do not offer much flexibility to modify the model as the situation changes, or test new models over the same camera network.

Researchers at the Indian Institute of Science (IISc.) have developed a software platform from which, the institute says, apps and algorithms can intelligently track and analyse video feeds from cameras spread across cities, which in turn would prove to be useful not only for tracking missing persons or objects, but also for “smart city” initiatives such as automated traffic control.

Yogesh Simmhan, Associate Professor in the Department of Computational and Data Sciences (CDS), and his team developed ‘Anveshak’, which not only runs these tracking models efficiently, but also plugs in advanced computer vision tools and intelligently adjust different parameters, such as a camera network’s search radius, in real time, an IISc. release said.

‘Anveshak’ can be used to track an object across a 1,000-camera network. A key feature of the platform is that it allows a tracking model or algorithm to focus only on feeds from certain cameras along an expected route, and tune out other feeds. It can also automatically increase or decrease the search radius or “spotlight” based on the object’s last known position. The platform enables the tracking to continue uninterrupted even if the resources ‒ the type and number of computers that analyse the feeds ‒ are limited, the release said.

“In 2019, as part of a winning entry for the IEEE TCSC SCALE Challenge award, Simmhan’s lab showed how Anveshak could potentially be used to control traffic signals and automatically open up “green routes” for ambulances to move faster. The platform used a machine learning model to track an ambulance on a simulated Bengaluru road network with about 4,000 cameras. It also employed a ‘spotlight tracking algorithm’ to automatically restrict which feeds needed to be analysed based on where the ambulance was expected to go,” the release explained.

The researchers are now working on incorporating privacy restrictions within the platform, as well as ways to use Anveshak to track multiple objects at the same time.

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Printable version | Feb 24, 2021 11:25:09 PM |

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