Video content analysis – research trends 2020
A strong video marketing drive is fundamental and is only set to become more so with predictions that consumers will watch 100 minutes of video every day by next year. This means that companies are having to think not only about how to create and upload video content in the first place but also how to make use of the video footage they have. And, as we’re seeing time and again, video content analysis research is undeniably the best way to make that happen.
Video content analysis is the extraction of metadata from video that can then be used to process the content of the video. This is translated into different applications and has many uses, ranging from search to event detection. It’s a very useful tool to parse videos quickly and efficiently.
Video content analysis has many benefits. It’s used in a number of industries, from market research to media and even in law enforcement. It helps pinpoint details — an otherwise time-consuming task if done manually — meaning effort and time is saved. The filters in place can also be tailored to suit your needs, making processes simpler for your business.
By relying on a wide range of methods, successful video content analysis research efforts can lead to increasingly accessible video-based data that would have once remained out of reach. Successful implementation of these techniques alone could even see you joining the 88% of marketers who report a positive ROI as a result of their efforts in the video sphere.
The trouble is that, along with every other digital focus, video content analysis trends seem to change all the time. That can make it challenging to keep on top of, especially considering that last year’s trends may no longer be relevant. For the most part, research and keeping a keen eye on the top research contenders are your best bets for success. And, to make sure you can at least pave a path through the growing 2020 video landscape, we’re going to look at the analysis research tools that are impacting video content right this second, and how you could enjoy their benefits.
# 1 – Object detection
It’s impossible to consider video content analysis right now without first thinking about object detection. As the name suggests, this video frontrunner involves the automated detection of key objects within any given video sequence based on your input. Whether you’re analysing marketing interviews or attempting to perfect video campaigns, this deep learning-led technology can drastically reduce the time you spend searching for specific objects.
Most notably from a marketing point of view, the ability to highlight how often particular objects appear can help you to tailor content to your prime audience. This object-led focus can also help you to easily track objects within a video sequence to ensure that you’re collecting all relevant information without the distractions you’d otherwise expect from your analytics efforts. This means that even large video warehouses will be easier than ever to access and analyse for improvements across the business board.
# 2 – Facial recognition
Using largely the same deep learning processes, facial recognition is also quickly coming to the fore in all things video analysis. By focusing on individual facial features or expressions, this technique allows for the identification or verification of an individual within any given video sequence and is primarily made possible by comparing selected features within an existing database.
The most apparent benefits of this undeniably come from a security standpoint, and facial recognition is now used with rigour across the security sectors, but there’s plenty of benefits here for marketers too. Without once risking invasive or potentially privacy breaching methods, facial recognition ensures that you can easily return to a specific research interview, or even build some idea of your returning customers/the habits of specific key audience demographics. Fashion retailers seem to be especially enjoying the benefits of efforts here, with 59% of UK companies in the sector now reporting significant returns as a result.
One thing’s sure, with the facial recognition industry alone set to reach heights of around £5.6 billion by 2022, marketers should be considering any potential benefits to their efforts here sooner rather than later. That means implementing as many difference recognition methods as possible, including 3D recognition, skin texture analysis, and more.
# 3 – Speech detection
What you see does, of course, matter a great deal to video analysis, but it isn’t the most essential aspect to consider. After all, those images are only as valuable as the sound that goes alongside them. In everything from your video content itself to consumer interviews and beyond, this is really where your main wealth of analysis gold lies. As such, speech detection also deserves a fair amount of attention throughout your efforts here.
Not to be confused with voice detection (more on that later), speech detection is concerned with the individual words spoken in any video sequence. Most notably, this technique will allow you to determine between spoken and silent areas of video content. This can prove fundamental for helping you to cut out white noise, or even head straight to any necessary information when analysing interviews, etc.
Automation is, again, growing in popularity here in the form of automatic speech recognition (ASR), but many are reluctant to get started due to accuracy rates that can be as low as 80%. As such, some companies are still finding that getting speech detection right is all about using human-led transcription services. Such services provide an easy-to-access, reliable speech detection framework that makes analysis possible despite multiple speakers, background audio, and a whole host of other potential speech detection setbacks.
# 4 – Voice detection
While speech recognition is concerned with what’s being said, voice detection capabilities are concerned with who is saying it. Also known as ‘speaker detection’, such methods are, therefore, more in keeping with the identification focus of facial recognition. And, just like facial recognition, a focus here can help you to verify video speakers or determine the identity of unknown parties that may appear in the background of a video clip.
Again, these methods can prove invaluable for helping you to distinguish everything from regional identifiers to repeat customers. And, again, transcription can make this possible. ASR is definitely a technology to look out for in this sense, allowing as it does for the unique tracking of certain tones, speaking patterns, etc. but a lack of reliability is still an issue here, especially where accents or multiple speakers are concerned. As such, human transcription can again help to pick up on cadences and individual speakers that guarantee you’re always getting the most from your voice detection data.
Contextualise with video content analysis
These trends may have a fair amount in common when you break them down, but the one theme that keeps arising to the forefront as the most valuable analysis aid is that of contextualisation. By taking video content and putting a voice, face, or label on what you’re seeing, you can finally add the context necessary to extract value from even seemingly mundane security footage.
What’s more, you can ensure that your all-important online video efforts forever hit the mark you’re after where consumers are concerned. Ultimately, as the video content you can expect to see/create continues to increase exponentially this year, your focus should be on making every clip as searchable and traceable as possible. And each of the tools mentioned here can help you do just that!