103rd Day Of Lockdown

Maharashtra2000641080828671 Tamil Nadu107001605921450 Delhi97200682563004 Gujarat35398254141926 Uttar Pradesh2655418154773 Telangana2231211537288 Karnataka215499246335 West Bengal2123114166736 Rajasthan1975615663453 Andhra Pradesh186978422232 Haryana1669012493260 Madhya Pradesh1460411234598 Bihar11860876590 Assam11002674414 Odisha9070622446 Jammu and Kashmir82465143127 Punjab61094306162 Kerala5205304826 Chhatisgarh3161252614 Uttarakhand3093250242 Jharkhand2739203514 Goa16848256 Tripura155812021 Manipur13256670 Himachal Pradesh104871510 Puducherry94644814 Nagaland5782280 Chandigarh4663956 Arunachal Pradesh252751 Mizoram1601230 Sikkim101520 Meghalaya50421
Decaf 03 Sep 2017 Tech that: Size up a ...

Tech that: Size up a crowd the neural way

DECCAN CHRONICLE. | B R SRIKANTH
Published Sep 3, 2017, 4:14 am IST
Updated Sep 3, 2017, 4:14 am IST
Convolutional Neural Networks (CNNs) are a subset of neural networks that are better suited to process and recognise images.
Prof. Venkatesh Babu along with his team from the Department of Computation and Data Sciences, Indian Institute of Sciences, Bengaluru. (Photo: DC)
 Prof. Venkatesh Babu along with his team from the Department of Computation and Data Sciences, Indian Institute of Sciences, Bengaluru. (Photo: DC)

Just how do you count crowds, stand by with an emergency evacuation plan to prevent a catastrophe triggered by a stampede, and all in a few seconds? Technology can help, say experts.

More than 2,400 people have died in crowd disasters in India between 2001 and 2014. In the first Kumbh Mela, post-Independence, in Allahabad in 1954, more than 800 people died and 2,000-odd were injured in a stampede caused when some people pushed through the barricades set up for a procession.

 

In the subsequent Kumbh Melas and other religious gatherings across the country, a sudden surge by devotees has claimed scores of lives because of lack of crowd control mechanisms and competent management by civic authorities and law enforcement agencies.

Given the fact that so many lives are at stake, how should the civic authorities plan for safety? How do the authorities concerned arrive at a conclusion that there are more people than the security and civic agencies can handle? What would happen in case of a repeat of the blaze at Puttingal Devi temple complex near Kollam, in Kerala, which claimed more than 100 lives last year?

Technology can help manage the crowd and plan for disaster contingency, say scientists at the Indian Institute of Science (IISc), Bengaluru. Prof. Venkatesh Babu and his team at the Department of Computation and Data Sciences have developed a novel method that can accurately estimate the number of people in densely crowded gatherings with the help of neural networks.

Neural networks are computational models inspired by biology that enable a computer to learn from observational data, just like the human brain. They look at thousands of sample data, and learn to recognise patterns. So they are used in applications involving image recognition, speech recognition and natural language processing.

Convolutional Neural Networks (CNNs) are a subset of neural networks that are better suited to process and recognise images. The researchers have tested their method with standard crowd-scene datasets, each containing multiple images of dense and sparse crowd scenes.

“Our method works well for scenes with extremely dense crowds and when the crowd density varies within the scene. This variation in crowd density within a scene is typical of crowded scenes that one might observe in the real world,” says Prof. Venkatesh Babu.

While there are many methods proposed to estimate crowd numbers, the advantage of this method over earlier ones is that it addresses scenes with varying crowd density very well.

“Our technique uses an expert CNN to choose the best CNN from a group of CNNs. This helps it employ different CNNs for different parts of the scene. And our training method achieves this automatically,” explained Prof. Venkatesh Babu.

He said a hand-held device would suffice to gather the data about the crowd, beam it to a high-end Graphics Processing Unit (GPU) and a Central Processing Unit (CPU), and immediately receive an estimate of the size of the gathering.
“Once the size of the crowd is known, security officials can take precautionary measures to avoid a crowd disaster. A GPU costs `60,000, while the other equipment is reasonably priced. So the entire system will not be very expensive,” he added.

Prof. Venkatesh Babu and his team presented their work at the ‘Computer Vision and Pattern Recognition’ (CVPR) conference at Honolulu, Hawaii, US, in July 2017. The team, however, plans to offer the software to a company that can develop a commercial product because “we are an academic research lab and focus on innovation and pushing the limits of performance of our techniques”.
Besides aiding security measures, it could be employed for civic planning, vehicle/traffic management and organisation of events. And, of course, to shoot down controversies like the size of the crowd at the Presidential inauguration of Donald Trump!

...




ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT