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Smart City Video Platform Finds Crimes and Suspects

"Investigator" program intelligently sifts through hundreds of video feeds and quickly homes in on persons of interest

2 min read
CCTV cameras outside the Chatrapati Shivaji Terminus (CST) railway station in Mumbai, India
CCTV cameras outside the Chatrapati Shivaji Terminus (CST) railway station in Mumbai, India
Photo: Dhiraj Singh/Bloomberg/Getty Images

In many cities, whenever a car is stolen or someone is abducted, there are video cameras on street corners and in public spaces that could help solve those crimes. However, investigators and police departments typically don’t have the time or resources required to sift through hundreds of video feeds and tease out the evidence they need. 

Aakash Khochare, a doctoral student at the Indian Institute of Science, in Bangalore, has been working for several years on a platform that could be useful. Khochare’s Anveshak, which means “Investigator” in Hindi, won an IEEE TCSC SCALE Challenge 2019 award in 2019.  That annual competition is sponsored by the IEEE Technical Committee on Scalable Computing. Last month, Anveshak was described in detail in a study published in IEEE Transactions on Parallel and Distributed Systems.

Anveshak features a novel tracking algorithm that narrows down the range of cameras likely to have captured video of the object or person of interest. Positive identifications further reduce the number of camera feeds under scrutiny. “This reduces the computational cost of processing the video feeds—from thousands of cameras to often just a few cameras,” explains Khochare.

The algorithm selects which cameras to analyze based on factors such as the local road network, camera location, and the last-seen position of the entity being tracked.

Anveshak also decides how long to buffer a video feed (ie., download a certain amount of data) before analyzing it, which helps reduce delays in computer processing. 

Last, if the computer becomes overburdened with processing data, Anveshak begins to intelligently cut out some of the video frames it deems least likely to be of interest. 

Khochare and his colleagues tested the platform using open dataset images generated by 1,000 virtual cameras covering a 7 square kilometer region across the Indian Institute of Science’s Bangalore campus. They simulated an object of interest moving through the area and used Anveshak to track it within 15 seconds. 

Khochare says Anveshak brings cities closer to a practical and scalable tracking application. However, he says future versions of the program will need to take privacy issues into account.

“Privacy is an important consideration,” he says. “We are working on incorporating privacy restrictions within the platform, for example by allowing analytics to track vehicles, but not people. Or, analytics that track adults but not children. Anonymization and masking of entities who are not of interest or should not be tracked can also be examined.”

Next, he says, the team plans to extend Anveshak so that it is capable of tracking multiple objects at a time. “The use of camera feeds from drones, whose location can be dynamically controlled, is also an emerging area of interest,” says Khochare.

He says the goal is to eventually make Anveshak an open source platform for researchers to use.

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Quantum Error Correction: Time to Make It Work

If technologists can’t perfect it, quantum computers will never be big

13 min read
Quantum Error Correction: Time to Make It Work
Chad Hagen

Dates chiseled into an ancient tombstone have more in common with the data in your phone or laptop than you may realize. They both involve conventional, classical information, carried by hardware that is relatively immune to errors. The situation inside a quantum computer is far different: The information itself has its own idiosyncratic properties, and compared with standard digital microelectronics, state-of-the-art quantum-computer hardware is more than a billion trillion times as likely to suffer a fault. This tremendous susceptibility to errors is the single biggest problem holding back quantum computing from realizing its great promise.

Fortunately, an approach known as quantum error correction (QEC) can remedy this problem, at least in principle. A mature body of theory built up over the past quarter century now provides a solid theoretical foundation, and experimentalists have demonstrated dozens of proof-of-principle examples of QEC. But these experiments still have not reached the level of quality and sophistication needed to reduce the overall error rate in a system.

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