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Enterprises step up use of video analytics for business efficiency

Enterprises step up use of video analytics for business efficiency
Photo Credit: 123RF.com

Adit Chhabra, CEO and co-founder of Wobot.ai, a Delhi-based video analytics company, is a happy man. He has seen a phenomenal rise in demand for its video analytics solutions in the past year. 

“After the pandemic, demand for remote monitoring solutions, especially video analytics, heightened to help with compliance with covid-19 norms. What we are seeing now is a larger awareness about what video analytics can do,” Chhabra said.

Pankaj Gupta, founder and CEO of EnableX, a video analytics and face recognition platform, has a similar experience with the pandemic contributing significantly to growth in business. “Our revenue has grown around 8x year-on-year (YoY), as of Q3 2021. About 70% of our growth can be attributed to the covid-19 pandemic, both in terms of clients and volume of usage among them,” he said. 

Chhabra and Gupta are not alone. The use of video analytics by enterprises and businesses in India has matured beyond covid-19 compliance, giving many companies that provide video analytics' services, cause to rejoice. Their services are now being used in various sectors in core operations. 

In the food sector, for instance, video analytics is helping deliver faster services. It's being used in manufacturing for worker and equipment safety; to reduce waiting time for patients in hospitals; and optimize retail operations. 

Video analytics tools process digital video signals captured through closed circuit television (CCTV) cameras and use algorithms to detect anomalies. It can be classified into three categories: fixed algorithm analytics (algorithms written to detect specific behavior), artificial intelligence (AI) learning algorithms (learns for the camera feeds to detect what is normal and then report any break in the pattern), and facial recognition systems (extract data points from face to create a digital signature to match with online database).

Atul Rai, CEO, and co-founder at Staqu has seen demand for face recognition mostly from the government sector. “In the private sector, most organizations are using video analytics for activity recognition,” he added. 

“Video analytics is being used for providing advanced security, process optimizations, and quality checks, across retail, healthcare, building, and construction, as well as other industries and public sector,” said Prashanth Kaddi, Partner, Deloitte India. 

A few examples of such applications include touchless office access, reduction of patient waiting times at healthcare units, customer behavior and product assortment at retail stores, inspection, and diagnosis of pipeline condition at energy and utility companies, according to Kaddi. 

The use of facial recognition technology (FRT) has been mostly in the public sector. According to data gathered by Internet Freedom Foundation (IFF) through its (right to information) RTI filings, 75 FRTs are currently installed across India by various central or state government agencies including government schools, airports, railways. Their use is largely limited to surveillance and verification of identity. 

Privacy advocates have slammed the use of FRT for law enforcement as in the absence of a 100% accuracy, misidentifications due to false positives, are likely to cause harassment to citizens. Their skepticism for the technology also stems from the fact that India still doesn’t have any guidelines to regulate the use of FRT. 

To allay any privacy-related concerns over the use of video analytics in private companies, solution providers are asking companies to ensure employees are aware and have consented to the use of the invasive technology.  

Chhabra said we request all our customers to ensure they have all authorization from the employees before deploying face recognition.

For some, growth in business has helped in diversifying their product. Gupta explains EnableX’s initial product could identify seven basic emotions among people. With more clients coming in, we could use the data we had to break down the service into 90 granular emotions, with a further layer of complication in terms of combined emotions – such as a customer being excited and nervous at the same time.

Experts believe the growth they have seen is here to stay as more businesses are becoming aware of its capabilities.