Video Big Data (Part 2) - What kind of Video Data?

In the last installment, we explained:

• Why Video Big Data will absolutely dwarf current Big Data
• How Video is the most difficult medium to extract data from

Which explains why Video Big Data remains a largely unexplored field. But also means the intense opportunities available because we have not even scrap the tip of this huge data iceberg.

In this installment, we will examine the kind of data elements that we can extract from videos. 

1. Speech
In a hour of video, a person can say up to 9,000 words. So imagine the amount of data just from speech alone. However, the process of transcribing speech is filled with problems and we are currently only starting to get an acceptable level of accuracy.

2. Text
Besides speech, text is probably the second most important element inside videos. For example, in a presentation or lecture, besides speech the speaker would augment the session with a set of slides. Or news tickers appearing during a news broadcast. 

3. Objects
There are thousands of objects inside a video within different timeframe. Therefore, it can be quite challenging to identity what objects are in the video content and in which scene they appear in. 

4. Activities
The difference between video and still images is motion. Different video scenes contain complex activities, such as “running in a group” or “driving a car”. Ability to extract activities will give a lot of insight what the videos are about. This includes offensive content that might contain nudity and profanity.

5. Motion
Detecting motion enables you to efficiently identify sections of interest within an otherwise long and uneventful video. That might sound simple, but what if you have 10,000 hours of videos to review every night? That’s a near impossible task to eyeball every video minute.

6. Faces
Detecting faces from videos adds face detection ability to any survelliance or CCTV system. This will be useful to analyze human traffic within a mall, street or even a restaurant or café. When we include facial recognition, it opens up another data dimension.

7. Emotion
Emotion detection is an extension of the Face Detection that returns analysis on multiple emotional attributes from the faces detected. With emotion detection, one can gauge audience emotional response over a period of time.

This list of video data is certainly not exhaustive but is a definitely a good starting point to the field of Video Big Data. In the next installment, we will examine some of the techniques used to extract these video data. 

Yours sincerely,

The VideoSpace Team

Video Big Data (Part I) - An Introduction

Fact: YouTube sees more than 300 hours of videos uploaded every minute. That's 18,000 years worth of videos in a year. And that's just YouTube ONLY! If we add all other videos in the public domain, we wouldn't even know where to start with the numbers. 

However, the even bigger numbers are actually hidden in the private domain from sources like broadcasters, media companies, CCTVs, GoPros, bodycams, smart devices, etc. We are recording videos at an unprecedented pace and scale. 

There is one word to describe this - BIG!

Which brings us to Video Big Data. Or should I say the lack of it. Even the term "Video Big Data" is rarely heard of. The reason is pretty simple - this stems from the inability to extract video data and making sense of it. But there is so much information embedded inside videos that is waiting to be discovered, it's an absolute goldmine! 

So the real question is... how can we extract value from videos?

However, the problem with video is that it is the most difficult medium to work with. There are a few reasons why: 

• There are so many elements inside a video (speech, text, faces, objects, etc)
• It is not static.
• It is very difficult to extract the various elements of video data. 
• Each video element requires a different data extraction technique.
• It is very difficult to make sense of video data because of its unstructured nature.
• It's expensive to extract data at scale

These problems are real and is preventing the arrival of Age of Video Big Data. But there is hope yet. With substantial use of Artificial Intelligence, VideoSpace is beginning to crack this enigma. 

In the next segment of this "Video Big Data" Series, we will examine how we can tackle these problems and extract value from videos.