Article Categories

Video Analytics with Hadoop

Insight on Performing the Video Analytics on Hadoop!

Data is widely available in two forms, known as structured and unstructured. Considering the present scenario where huge amount of data is flooded every minute, everything about big data video analytics needs to be understood.

Let’s learn more about the same.

Big Data Video Analytics

If you have heard of the big data courses in Delhi, you must have a little idea of big data video analytics. There are various analytics tools for use on the structured data and analysis of unstructured data in the video format is still an area needs to be discovered as far as analysis is concerned. Use of video recording gadgets has been rapidly growing which as a result increasing the data and also the need to analyze the same.

A quick look at the data gathered around the world shows that 80% of all the data is available in unstructured format. The challenge is that the presently available analysis tools can only analyze the structured data.

Another data reveals that YouTube has been getting uploads of a huge amount of video data with each passing day. This huge number of data needs another solid analytical tool for analysis.

Significance of Hadoop

Here Hadoop comes in picture which plays an important role in solving the issue of analysis of big video data. The success of Hadoop in an analysis of structured data naturally attracts the interest of various stakeholders. They strongly believe the power of Hadoop which can effectively analyze even the unstructured video big data.

Some of the concepts known as Transcoding and MapReduce Architecture are important and come handy to help in the analysis of unstructured video big data. However, using Hadoop comes with certain limitations with regards to structured query capabilities. Hadoop should also improve its capabilities to be efficient to start the analysis of the big data. Hadoop training in Delhi can be really helpful in such scenario.

Digital devices which produce millions of pixels in a flash are in the pockets of billions of people around the world. If you look around, there are other forms of video data other than YouTube. These may include Surveillance video recording etc. The video recording devices further generate data which will need analysis. There have been researchers working on to find out how the analysis of the unstructured video and image data will work.

At most organizations, the security devices operate 24*7 and archive the recent ‘hot data’ for future investigation. An ordinary enterprise will produce about a terabyte of video with each passing day and that too, from multiple sites around the office premises. Not only this, there are companies that are getting storage solutions to Fortune 500 clients.

So we can understand the amount of data being produced every single minute. With such great amount of data comes great responsibility of managing the same and especially analysis.

It has in turn, given a rise to the need to manage huge amount of video data. IT departments in large scale enterprises are now uniting datasets which currently store in silos. It is the high time we dig into the datasets for the insight.

Hadoop institute in Delhi is helping people to deal with the demand of the hour. They have various courses that enable professionals learn how to analysis the video data. Now software solutions more concentrated on real time analytics including motion detection and counting vehicles on highways instead of the insight-specific analytics or in-depth analytics.

These solutions are known for processing the video stream efficiently and that too in the real time. It is probably the only time when analytics algorithms get in touch with these data. The metadata generated will be related to triggering alarms whereas the video data needs to be stored for a short time in an archiving file system.

Challenges Ahead!

Now we have a fair idea about the role of Hadoop in performing the video data analysis. Even though, performing this with Hadoop is not that easy. The challenges are ahead which include:

Video Transcoder

First and foremost, the challenge which comes the way is to decide on the way to deal with compressed video data, suffering from various legacy limitations. Long time back, the MPEG standard was recommended for efficient encoding and decoding the sequence of the image frames along with intra-frame coding to provide high quality video streams which is bounded by transmission bandwidth. The main obstacle is that the MPEG could not predict the Big Data revolution decades ago. Then, the MPEG compressions appear unfriendly to mainstream distributed systems like Hadoop or MPI. The solution is the smart MapReduce jobs which can seamlessly decode every video chunk on HDFS in a distributed way.

Video Analytics

The video data is required crunching into image frames and then performing analytics on the data which is Hadoop-friendly. No doubt, Hadoop MapReduce comes as a strong scalable technology. It can be done if it is carefully dissecting the typical video analytics system. MapReduce enables to help in providing linearly scalable performance that needs little effort to craft parallelism.

SQL Analytics

The most common investigation are done post event that takes place by surveillance video. The efforts are put in by security officers who manually do this tiring task. Having a strong video analytics platform can leverage the structured insights Hadoop offers by using an efficient query language like SQL.


About the Author

Sunil Joshi

Sunil Joshi

We provide best hadoop training in Delhi

(Show Bio)

Reader Comments