Significance of Video Analytics on Hadoop

As a matter of fact, Big Data is no longer a strange term. The worldwide organizations and the world of businesses recognize it as one of the rapidly growing area in the Information Technology. Interestingly, the world keeps getting flooded with data where 90% of the data belonging to the world has been created in less than two years. 80% of those data happens to be the unstructured data, mostly videos and photos.

In today’s situation, the most popular Big Data modalities are designated to handle structured data. For example, Stock pricing prediction in Financial Services. In recent years, these platforms got equipped to handle certain semi-structured data such as text data from social platforms like Twitter and social graph data from “Thumbs-ups” and the “Likes” of Facebook and LinkedIn. But the significant new data modality is yet to be concentrated. Unstructured data, being a major source of world’s data demands much more attention in the current Big Data market.

These unstructured big data include enormous volumes of pixel data generated from digital devices in a flash are used by the billions of people worldwide. Several online video archiving systems, such as YouTube witnesses its users uploading at a range of 100 hours of video every minute!  Be it be surveillance cameras in the office or Cameras attached with the traffics and roads, the list goes on and on. Business and organization are in need of Hadoop to analyze and process these videos and photos to render a useful stats and business data.

Hadoop Video Analytics

The exclusive open-source framework that is renowned for processing unstructured big data with just a flash of seconds, Hadoop is one of the most sought-after video analytics platforms by the data analysts across the globe. It is more focused on enabling real time analytics (motion detection, counting vehicles on highways, etc.) and greater insight-oriented analytics or in-depth analytics.

Challenges in video analytics

Performing this complicated video analytics on Hadoop is not an easy game. There are multiple challenges to faced and following are some of them,

Legacy and Quality

Have to deciding on how to deal with those compressed video data which could be suffering from a number of legacy limitations. And it is followed by the efficiency of decoding/encoding of the video frames with respect to their quality.

Deep analytics

After crunching the unstructured video data into several image frames, the next step would be the performing of deep analytics on those Hadoop-friendly data. Powerful tools like Hadoop’s MapReduce would come to the rescue.