In a business setting, analyzing video data helps you measure ad engagement, track shopping patterns, ensure employees follow safety protocols, automatically inspect product quality, and more.
Essentially, analyzing video data unlocks insights across departments, including the sales, marketing, human resource, and security department. However, what happens when you don’t understand what video analytics technique is appropriate to achieve a specific objective?
If you don’t understand what a video analytics technique entails, you waste time and money using the wrong analytics tools. You also risk a technique-objective mismatch, leading to irrelevant, incomplete, or misleading insights. That’s why we’ve prepared this piece to clear up the confusion around what technique to rely on and when.
Video Analytics Techniques and Example Applications
While you can manually analyze video data, this approach is slow and limited. So, we use AI-powered tools to analyze videos. These tools employ various video analytics techniques to unlock insights that improve services, power innovations, and optimize decision making.
Before analyzing video data, ensure you have a clear objective in mind. Then, establish a step-by-step process outlining how to examine video data to achieve the objective. Doing this should help you match one or more of these analytics techniques with an objective and select a suitable video analytics tool.
Here’s a breakdown of the most employed video analytics techniques:
Video summarization
Sometimes, we just want a quick summary of a video to determine its relevance in a particular context. Or, we want to work with specific parts of a video only. That’s where video summarization algorithms come in!
Video summarization algorithms are either extractive or abstractive.
The extractive ones select key segments of a video, like moments with specific objects or unusual activity, and stitches the segments together.
For instance, when collecting video data for AI training or fine-tuning, an extractive approach reduces the time used to determine whether the obtained videos check the right boxes.
The abstractive algorithms, on the other hand, generate a compressed reconstruction or narrative that conveys the primary essence of the video. Such algorithms are in AI tools. That’s why they at times use footage that is not in the original video.
Facial recognition
Thanks to deep learning algorithms and neural networks, there are systems that can identify or verify people in a video.
Facial recognition algorithms use various calculations to map out facial features. For example, the distance between your eyes, jawline contour, and nose shape.
The algorithms also pick up these calculations in different conditions, ensuring that they can identify a person in different angles, varying lighting, or even when they put on a mask or glasses.
Facial recognition comes in handy in the security department, allowing you to ensure that only authorized personnel access restricted areas. You can also use it in marketing and sales to identify returning customers or analyze emotions.
Object detection and recognition
Object detection and recognition is powered by computer vision and artificial intelligence.
Computer vision systems are trained on large datasets of images, making it possible for them to pinpoint objects in a video. Most of these systems draw boxes around the objects they can locate in a video.
Artificial intelligence aids with object recognition. There are models trained to identify objects based on textures, colors, and shapes.
Combining both object detection and recognition systems gives us modern systems that you can use to detect hundreds of objects. The systems can tell objects from each other, from vehicles to everyday products and items.
With object detection and recognition systems, you can monitor activities in real-time. For example, you can use the systems to distinguish delivery packages or detect safety helmets on workers.
Optical Character Recognition (OCR) in video
Want to extract insights from certain text in a video? OCR in video is the go-to video analytics technique. Computer vision, natural language processing, and deep learning algorithms make OCR in video possible.
Computer vision systems isolate potential text regions in video frames. They do this by checking for features like text shapes, edges, and contrast. After isolating the text regions, the system processes the findings to identify numbers, symbols, or letters.
Natural language systems help with making sense out of the text. Is it a number place, a product number, or something else?
Deep learning systems, on the other hand, have advanced the accuracy in video OCR. These systems can handle variations in languages, orientations, and fonts. They can even identify obstructed text or text in blurry frames because of the sheer amount of data these systems learn from.
Contextual analysis or scene understanding
Some call it the step towards intelligent video analytics. Unlike object detection and recognition, scene understanding involves the use of deep learning algorithms to explain what’s happening within an environment.
Deep learning contextual analysis systems aim to understand how objects in a scene relate to each other, their surroundings, and the broader context. It is not just about detecting objects anymore, it is about analyzing multiple layers of information to build a “narrative” of what’s happening.
Since contextual analysis systems can check object location, environment conditions, movements, and even cultural cues, they are the most advanced video analytics systems currently. You can use them in security to enhance surveillance, retail to understand customer behavior, and in many other business scenarios.
Innovations Shaping Video Analytics
Video analytics systems are getting better thanks to these innovations:
Generative AI and self-supervised learning
Generative AI models are being trained on video data, making it possible to generate footage based on a prompt. In video analytics, generative AI makes it possible to fill gaps when footage is incomplete or unclear.
Moreover, more effort is going into building self-supervised learning AI models for video analytics. Such models learn and improve on their own, becoming better at understanding scenes, predicting scenarios, and adapting to new environments.
Real-time analytics
Smarter algorithms, edge computing, and faster processors make it possible to capture footage and analyze it as the events unfold.
Cameras record and send the data to systems that quickly analyze motion, recognize behavior, or detect objects the moment footage is captured. This has especially improved security systems that detect break-ins or fires.
3D and augmented reality (AR)
3D and augmented reality technology is now being used to add depth to normal footage.
3D analysis systems can measure distance and depth more accurately. This way, cameras don’t just see what’s present in a video, they can tell the length of an object, how far away an object is from another, or whether an object is obstructing another.
After analysing an environment, 3D systems overlay digital information on top of live footage. This is mostly useful when you want to guide people around a specific environment.
Final Words
Thanks to computer vision, generative AI, deep learning, and innovations like 3D analysis, we no longer have to waste time and money on manual video analysis. These technologies power various video analytics techniques as discussed.
Take your time to understand what the video analytics techniques are all about to avoid a technique-objective mismatch. And, as you employ the mentioned analytics techniques, remain ethical. Don’t use the available video analytics solutions to infringe people’s privacy.
