Historical and real-time enterprise video is growing rapidly. While the data can create immense value for organizations, the large data volume makes it impossible for humans to process the data. A near-real-time analysis is necessary to make effective decisions and take actions, creating high dependence on enabling technologies, including video analytics.

The COVID-19 pandemic also drives businesses to recognize video’s value and increase technology investments to access insights and achieve business outcomes. While the pandemic has led governments and organizations worldwide to adopt facial recognition technologies, video analytics needs a holistic approach in device management, solutions, infrastructure, and data security to derive maximum value.

As artificial intelligence (AI) capabilities mature amid the rise of machine learning, deep learning, availability of edge infrastructure, and application programming interface (API) integration, video analytics capabilities are registering a usage surge. Video analytics adoption is growing in various sectors and industries, with face recognition at the core and enabling more accuracy. Vendors augment facial recognition with detection and analytical capabilities to classify and analyze facial expressions and human emotions. Combining digital technologies, behavioral science, and psychology allows enterprises to leverage emotional intelligence to gain strategic advantage. Thus, video analytics is finding new use cases, with emotion AI vital in driving its adoption.

The increasing video analytics applications offer significant opportunities to software and hardware vendors and service providers. Monetizing emerging opportunities necessitates revisiting and reshaping business models to fit current needs. Given the nascent stage and high potential of emerging technologies such as emotion AI, stakeholders must create niche industry use case-driven strategy and identify ways to encourage initial adoption.

Author: Nishchal Khorana