AI Is A Game Changer For Video Analytics

With the ability to efficiently assess voluminous data produced by videos, Machine Learning and Deep Learning algorithms have significantly spiked up the adoption of video analytics across industries

- Rajesh Dangi

The internet has an ocean of images and videos that exude diverse meaning or context to its creator. It can become a relevant source of information should there be a way this data can be read, analyzed to arrive at coherent information, and applied to derive meaningful applications, beneficial to a larger audience.

Video surveillance is a common technology owned by public and private entities alike to monitor their surveillance requirements. Video Analytics on the other hand is an advanced emerging technology that gathers, processes, and analyses the visual content from the CCTV/IP cameras, making the data an asset that can be put to meaningful use to empower businesses.

Evolution of Video Analytics

Nazi-occupied Germany was one of the first countries to possess video surveillance to observe the launch of long-range guided ballistic missiles. Later, the first video surveillance system was installed in 1942 for observing the launch of V-2 rockets (long-range ballistic missiles) but later on, evolved into other industrial use cases. Cut to the 1970s, the evolution of video analytics began with VCR technology making it easier for companies to record video camera feeds. However, this offering only facilitated the storage of data without the ability to read or derive any useful information from it.