To implement video analytics, organizations use a collection of technologies, including video processing, object detection, object recognition, and object tracking. Many of these technologies are based on deep learning algorithms trained on computer vision tasks.
Video Processing
Video processing technologies are responsible for extracting and analyzing information in video format, making it readily available for human users, autonomous systems, and robots. During the processing cycle, the video is read (often frame-by-frame) and then extracted as features . Specifically, each frame, which is an image, is turned into a matrix of numbers representing the color and position of each pixel.
You can create a manual video processing cycle, but this is very time consuming. To ensure efficiency, companies use mathematical functions and open source libraries to automate processes, often using machine learning algorithms. Notable open source resources include OpenCV and TensorFlow.
Object Detection
The main function of object detection is to analyze an image and search for objects within it. Object detection models treat an image or frame as an input, and then apply image classification and object localization techniques to detect the objects within the image. Typically, each detected object is surrounded by a bounding box and gets assigned a class.
Object Recognition
The main function of object recognition technologies is to train machines to accurately recognize objects, such as faces and cars. Object recognition is applied in video and image processing, as well as computer vision, and involves training machine learning models to identify each and all objects in a frame.
Object Tracking
The main function of object tracking is to monitor the behavior of objects over a certain period of time. This process uses object detection to locate and classify objects and then track the objects across a static video file or a live stream. Object tracking is a highly complex process that requires accurate identification of objects across a long duration of time.
3 Video Analytics Challenges and Solutions
Here are three key challenges faced by organizations implementing video analytics, and directions for resolving them.
Data Drift
Data drift, or content drift, occurs when training can’t keep up with changes to the input data, which ultimately results in low accuracy. When AI algorithms process data that is changing more frequently than the training data, there is a gradual shift in the distribution of data, which reduces their predictive power.
To avoid excessive drift, you should make sure your analytics models are updated with ongoing training. Continuously monitor the system, and when you detect lower accuracy, refresh the algorithm with new training data.
Complexity of Video Processing Tools
Video content differs from text or static images in a number of ways, but the biggest difference is the scale of data involved. Videos contain large volumes of data, as well as various types of data. Capturing, processing and analyzing video therefore requires the use of complex software tools and often dedicated, specialized hardware.
Effective video analysis depends on the quality of your tools—from the cameras used to record the footage to the analytics software that extracts metadata from it. It can be difficult to maintain the right balance of effective tools, given that both hardware and software have limited lifecycles and may require frequent upgrades.
Furthermore, the technology required for processing video content may have a steep learning curve for inexperienced teams.
To address this problem, many organizations use managed video solutions offered as a cloud service. Most cloud providers offer platform as a service (PaaS) solutions for video processing, transcoding, and delivery, and many of them also have advanced AI capabilities. Instead of building their own stack, organizations adopt these solutions, reducing the learning curve and upfront investment needed to pursue video analytics.
Data Storage
Video analytics requires the collection of massive amounts of data, and the amount of data being collected is only growing—some studies show video content processed by analytics solutions grew by 500% between 2015 to 2019. Storing all this data can be challenging, both in terms of resources and management. Another issue to consider is the increasingly stringent requirements for data privacy.
Again, a convenient and readily available solution is cloud services. Cloud providers like Amazon, Azure and Google Cloud provide a range of storage services, including low cost, infinitely scalable object storage, which is suitable for delivering video content to users, and high performance storage options such as managed attached drives, which can be used to locally cache video data for fast processing.
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