With the global expansion of digital technologies and world-wide-web based technologies, data has become the key enabler of innovation and growth. For all modern organizations, data is an invaluable asset that impacts every aspect of business ranging from customer experience to product design or sales strategy.
Humongous volumes of the data originate from the millions of CCTVs and IP cameras that are installed in public areas, transportation systems, and within premises of different enterprises. We are now in an age where cities are rapidly being converted into smart cities with the help of IoT and cloud etc. AI evolution has come across as a major enabler of processes such as extraction of actionable insights from raw video data generated by the CCTV networks. Primarily conceived as surveillance or monitoring mechanisms, today, the AI analytics of the video feeds has offered a number of smarter solutions and diversity of usage.
This type of intelligent video analytics technology uses deep learning neural networks to analyse video feed and autonomously become capable of identifying objects, people, activities and even emotions, either real-time or upon review.
Limitations of conventional analytics tools
Almost every business uses data and analytics to some extent to make informed and important decisions. However, video analytics is not yet an area that has been adequately focused on. The AI-powered video analytics is capable of capturing more data from everyday operations than regular analytics and leads to better business decision-making.
Let’s say, typically a business uses PoS data to understand the customer trends. Such an approach would be governed only with the transaction data and product sales. However, once you throw video analytics into the mix, you would be able to get data such as time spent in each section, average checkout waiting time, interest shown in products the customers liked but didn’t buy. These insights can then be used to enhance the customer experience.
For example, a retailer might traditionally use PoS data to learn about customer behaviour, restricting them to transaction statistics and sale logs. AI-powered video analytics can reveal how customers interacted with the entire store – time spent in each section, average wait time for checkout, even interest in products they liked but didn’t buy.
The manufacturing floor can be dangerous for workers, and supervisors can have a hard time identifying potential anomalies. An AI-powered system can help monitor video feeds and trigger an appropriate action based on insights thus improving product quality and worker’s safety.
This impact is slowly yet steadily dawning on organizations across the world. The video analytics market is growing at a CAGR of 22.75. From a market size of $4.10 billion, it is projected to reach $20.80 billion by 2027.
Usage of video analytics in different business environments
Video analytics has almost limitless potential of usage in almost every business stream. From protecting your premises from security threats to advanced surveillance, AI-powered video analytics can do almost everything. Integrated with big data and edge computing as well as the development of multi-spectral camera hardware, the use cases of AI analytics far exceed the conventional CCTV roles. Let’s take a look at some of these.
Healthcare industry: On-site video analytics is helping the medical services providers the ability to track patient inflow, manage waiting for time, and monitoring of emergency cases in real-time. The technology is of great use in remote monitoring of patients in isolation such as in COVID-19 wards to ensure the safety of the healthcare staff and other non-COVID patients.
Retail industry: Video analytics is capable of predicting customer interest by analysing the behaviour and actions of people in a store, and generating insights that help retailers enhance customer experience.
Manufacturing sector: Manufacturers can use video analytics for product tracing and generation of insights on manufacturing processes, prevention of manufacturing bottlenecks and delays which enhance efficiency.
Governance: Administrations of smart cities and local governments can automate traffic-surveillance and perform tasks such as vehicle identification, traffic redirection, locating stolen vehicles, crowd management and enforcement of masks and social distancing protocols during the pandemic times.
Banking sector: Video analytics integration can help raise security alerts if an unauthorized entry in restricted areas or any other suspicious activities as well as adherence of protocols in the wake of the pandemic.
Smart video analytics technology has the power to serve almost any need of the modern world ranging from smart cities, retail, and manufacturing to healthcare, etc. As AI and IoT technologies evolve, there will be greater adoption of video analytics by business organizations in an ethical, strategic and widespread manner to expedite their growth.
The following article has been written by Abhijit Shanbhag, President and CEO, Graymatics