What is Video Analytics?
Video analytics involves automatically recognizing patterns such as a temporal or spatial event in video footage. Video analytics solutions typically provide real–time monitoring capabilities, enabled by machine learning and deep learning.
Video analytics systems identify objects and movement patterns to help detect a range of event types. For example, video analytics can detect someone moving suspiciously or disobeying traffic signals. Alternatively, you could use video analytics to spot a fire outbreak or mine historical data for new insights.
Today, Deep Neural Network (DNN) technology enables the training of video analytics systems in a way that mimics human cognitive capabilities. Classic computer vision, based on simple statistical algorithms, has fast evolved into cognitive systems that can identify specific objects or object attributes and track their trajectory.
How Video Analytics Works
The power of video analytics lies in the ability to process the dense visual and audio data in video streams. Video analysis relies on the sequence and continuity of the video frames, which allow it to extract more insight than would be possible with an individual frame or still image.
Video analytics solutions process video data in two stages. First, the system detects movement in the footage ”it monitors changes in the pixels to identify where there is movement. A mathematical function enables the analytics system to detect objects in motion by tracking differences between frames ”any difference is seen as a movement.
Second, the analytics system recognizes a pattern and classifies the moving object, tracks its trajectory, and isolates it from other objects. This is a more complex task than simply detecting movement, so it relies on AI functions that identifies objects within a frame, classifies them, and defines their movement as a trajectory throughout the sequence of frames.
Top Trends for Video Analytics in 2021
Here are some of the key trends driving video analytics in 2021 and beyond.
1. AI
Artificial Intelligence is at the heart of modern video analysis methods. It enables the conversion of video content, whether pre-recorded or live-streaming, into meaningful metadata that can be used to extract actionable insights. Massive development efforts are invested around the world into developing AI and deep learning algorithms, producing ever more accurate and efficient video analytics capabilities.
2. Cloud-Based Video Analytics
Cloud-based video analytics is a commonly discussed trend, though the market remains relatively small. However, the ease and affordability of cloud deployments means the industry is growing. Cloud services help organizations save on the expenses of installing and maintaining on-premises deployments, and they are becoming increasingly secure.
Cloud providers offer packaged, platform as a service (PaaS) solutions that include powerful video APIs, large-scale storage for video, advanced analytics services (see for example those provided by AWS), computational resources with hardware acceleration like graphical processing units (GPUs), and built-in AI capabilities that can analyze data in video streams.
3. Real-Time Video Processing
Real-time video processing offers significant advantages over traditional video analytics, which is performed on video data in retrospect. For this reason, many organizations already using video analytics are expanding their capabilities to include real-time processing.
Increasingly, various business goals are requiring immediate responses to the data being generated, for example by video intelligence dashboards, and live streams from surveillance systems. The ability to receive immediate alerts and extract insights in real time provides new capabilities.
However, real time video processing is much more difficult to achieve, because it requires very fast processing of complex, deep learning algorithms using advanced GPUs. New types of algorithms are emerging, such as the YOLO object detection system, which provide a slightly lower level of accuracy with much faster processing speed. This is making it possible to apply deep learning analysis to video streams in real time, without a huge investment in computing infrastructure.
4. Facial Recognition
Facial recognition software correlates images of individual faces with a database of images. This form of biometric data has many important applications, ranging from identifying privileged customers to identifying crime suspects or missing persons. We can therefore expect to see the continued increased adoption of facial recognition technologies, along with improved analytics capabilities.
The widespread adoption of facial recognition software is becoming increasingly possible with the growth of high resolution imagery and improved video processing technologies. However, facial recognition solutions remain controversial, and regulations have yet to catch up with the technological reality.
5. High-Resolution Video
High-resolution video, defined as 4K or 8K, which refers to the number of pixels in the image, is becoming increasingly mainstream. Higher-resolution video content enables more advanced and accurate video analysis. Practical applications include identifying objects in a crowd, count-based alerts and facial recognition. Higher resolution means more accurate data, which can inform insights and enable real-time alerts and responses.
The main challenge to the mainstream adoption of high-resolution video processing technologies is that they are computationally intensive and require expensive hardware. However, over time the cost of hardware will go down, and video analytics for high resolution video should become more widespread.
6. Real-Time Automation
Video analytics is commonly used by real time operators, but can also be connected to automated systems. Real time video analytics are being integrated with rapid response systems ”for example, raising an alarm in a physical facility and alerting security teams when suspicious activity is discovered in a video frame.
Transport systems are also being integrated with video analytics, enabling automated adjustments to traffic lights, public transportation schedules, real-time adjustment of traffic lanes, and dynamic traffic signs.
Conclusion
In this article, I discussed the value and huge potential of video analytics, and covered six important trends driving the industry forward:
- AI-based processing of videos to derive more advanced insights
- Cloud-based video analytics providing managed platforms for video processing
- Real-time video processing to enable insights from live video streams
- Facial recognition, used to detect human activity in videos
- High-resolution video enabling more accurate object detection
- Real-time automation driven by insights from video streams
I hope this will be of help as you explore your organization‘s next steps in deriving value from video data.
