Video Big Data Whitepaper (FREE download)

The term "Video Big Data" is rarely heard of. The reasons are pretty simple: 

1. It's difficult to extract data from videos
2. It's difficult make sense of unstructured video data

Therefore, it is not an understatement to say that video is the most difficult medium to search and extract intelligence from. However, given the amount of videos are that generated daily in the public domain (e.g. YouTube) and private domain (e.g. broadcasters, CCTV, education, etc.), it is also not an understatement to say that video is the King of Content. 

The objective of Big Data is to gain Business Intelligence. Video Big Data is no different. The obvious difference is the source and the type of data that can be extracted out from videos.

This Video Big Data Whitepaper aims to explain how we can extract value and intelligence from videos with a 3 step approach:

1. Extract video data 
2. Transform unstructured video data
3. Analyse to data into intelligence 

With this whitepaper, we hope to share some of our knowledge and experiences working with Video Big Data. From our calculations, we estimate that Video Big Data will dwarf Big Data as we know it. Thus, the importance of this whitepaper. We hope you enjoy and benefit from it!

Yours sincerely,

The VideoSpace Team
 

Video Big Data (Part 3) – From Mess to Intelligence?

The objective of Big Data is to gain Business Intelligence. Video Big Data is no different. The obvious difference is the source and the type of data that can be extracted out from videos. In there, lies the main challenges - Extraction, Transformation and Analysis.

In this instalment, we will explain why Artificial Intelligence is central to the “mess” in video big data.

In the first installment (Part 1), we explained:

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

In the previous instalment (Part 2), we examined:

• the kind of data elements that we can extract from videos (speech, text, objects, activities, motion, faces, emotions)

But first, let’s examine why there is a mess in video data. The short explanation is because a large part of video data is unstructured data. In particular, data from speech and text. For example, text extracted from a 30 minutes news segment could cover multiple topics and events, mentioned numerous places and persons. To add to the complexities, we have to time-aligned when these words are spoken. In many ways, text (e.g. slide presentations that appear in videos) are the same.

Thus, we have to answer 2 key question:

1. How do we meet sense of ‘messy’ video data?
2. How can we extract knowledge or intelligence from that mess?

The answer lies in another form of Artificial Intelligence (A.I.) - the study of Natural Language Processing (NLP). That is because it can process and attempt to make sense of unstructured text in the following areas:

• Topic detection
• Key phrase extraction
• Sentiment analysis

The reason is because NLP can be used to turn unstructured video data into structured data. Only then can we start making sense and manipulating the data into either intelligence or actionable items like alerts, triggers, etc.

The field of Video Big Data is just starting. Without the advancement in multiple areas of Artificial Intelligence in multiple areas (Speech Recognition, Computer Vision, Facial Analysis, Text Analytics, etc.), Video Big Data wouldn’t even exist as it needs these fields to work in tandem or in sequence.

Given the rate that we are producing videos, alongside our ability to extract video data using A.I. The only way is up and we are not even close to uncovering the tip of Video Big Data iceberg.

Video Big Data will be bigger than BIG. 

VideoSpace will be right in the middle of it all. Let’s put this prediction into a time capsule and revisit it in a few years.

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 Babbobox Team

Video Big Data (Part I) - An Introduction

YouTube sees more than 300 hours of videos uploaded every minute. That's 432,000 hours in 1 day or 158 million hours in 1 year. 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, surveillance cameras, GoPros, bodycams, smart devices, etc. We are recording videos at an unprecedented pace and scale. 

There is one word to describe this phenomenon - 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. 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.  

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: 

• It is very difficult to extract various elements (speech, objects, faces, etc.) 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

But there is hope yet. We will examine how we can tackle these problems and extract value from video big data in the next article.