Vishal Dhupar, Managing Director, South Asia, NVIDIA
Can creating potential new data points ensure fair and balanced assessment? The first step in video analytics is to train artificial intelligence (AI). This requires AI to go through thousands of hours of video to 'learn' what different objects, such as people, cars, license plates and trucks are. Initially, a human must help the AI by tagging different objects, but very quickly the AI can self-learn and educate itself at a rate that is orders of magnitude faster than what people can do. The learning process needs to take place in a data centre that is enabled by the Nvidia DGX platform or on Nvidia GPUs (Graphic Processing Units) in the cloud. Once the AI understand and identifies objects, it can scan through the massive amounts of video data to quickly and accurately make inferences.
Historically, organisations did not trust video analytics because the process was slow and error prone. A person can look through so much data for only so long before fatigue sets in, causing them to easily miss an individual, a license plate or any other object, particularly in low light or bad weather.
How can data have a direct impact on day-to-day operations?
From license plate identification to emotion detection or object recognition, there are literally thousands of use cases for video analytics that are now made possible by the deep learning algorithms that can be performed on GPUs. Building lobbies could use facial recognition to scan people and direct them to the right elevator to get them to their meetings faster instead of having to stop at a desk with a human who needs to take a picture and access a separate system to correlate the information.
How can big data especially in smart cities improve and help manage revenue stream?
In a smart city, data is as important as the physical infrastructure. Beyond the hype around the big data, the next-generation analytics solutions will provide many industries with a new and very real opportunity to improve their operations and make a breakthrough in customer experience. This is particularly true of industries that have a large customer base or complex infrastructure such as retail, financial services, utilities and telecom. Big data analytics is a multi-fold opportunity, allowing for improved network utilisation, efficacy of operations and customer experience, as well as reducing operating expenses and creating new revenue streams.
What is your experience in this regard? How has the use of big data made a difference?
NVIDIA's Metropolis, an intelligent video analytics platform, makes cities safer and smarter by applying deep learning to video streams for applications such as public safety, traffic management and resource optimisation. More than 50 NVIDIA AI city partner companies are already providing products and applications that use deep learning on GPUs. Deep learning enables powerful intelligent video analytics that turn anonymised videos to offer real-time valuable insights, enhancing safety and improving lives. The NVIDIA Metropolis platform enables customers to put AI behind every video stream to create smarter cities.
- RAHUL KAMAT