A software platform that could help track missing persons: IISc Study

Researchers at the Indian Institute of Science (IISc) have developed a novel software platform from which apps and algorithms can intelligently track and analyse video feeds from cameras spread across cities.

The IISc said that such analyses are not only useful for tracking missing persons or objects, but also for “smart city” initiatives such as automated traffic control.

The breakthrough could benefit metros around the world who have set up thousands of surveillance cameras in public spaces.

“Machine learning models can scour through the feeds from these cameras for a specific purpose — tracking a stolen car, for example. These models cannot work by themselves; they have to run on a software platform or ‘environment’ (somewhat similar to a computer’s operating system),” the IISc said, adding that existing platforms are usually set in stone, and do not offer flexibility to modify the model as the situation changes, or test new models over the same camera network.

Yogesh Simmhan, Associate Professor at the Department of Computational and Data Sciences (CDS), added that the development was prompted by gaps in the existing technology such as models becoming overwhelmed if the search area is increased.

In response, Simmhan’s lab developed a software platform called Anveshak. It can not only run these tracking models efficiently, but also plug in advanced computer vision tools and intelligently adjust different parameters — such as a camera network’s search radius — in real time.

In a recently published paper, the researchers show how Anveshak can be used to track an object (like a stolen car) 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 also enables the tracking to continue uninterrupted even if the resources — the type and number of computers that analyse the feeds — are limited.

Addressing the matter of privacy infringement, Professor Simmhan said his lab was also working on incorporating privacy restrictions within the platform. “We can decide what are the kinds of analyses that we are comfortable running.

We could say, for example, that we would allow analytics that track vehicles, but not analytics that track people, or analytics that track adults but not children,” he said.