With the pace that the technology is getting upgraded, many new-age solutions have now been able to allow the user complete tasks in minutes that traditionally take hours to get done. An unprecedented explosion in data analytics has brought a significant difference in the way data was processed in recent years vs how it is processed and dealt with now. Mobiles, desktops, websites, and several IoT devices have made it possible to extract data from streaming videos in real-time. Imagine being able to build a report on a real-time basis or creating an application that alerts you as soon as an event happens just by using the streaming video available and extracting meaningful information from it. Isn’t it magnificent? Well, that’s exactly what a technique of video stream processing does.
It is a process of a continuous flow of data as it is generated from a video stream so that it can be used for real-time processing and analytics in various capabilities.
Why Is Video Stream Processing Helpful?
From an analytic and reporting perspective, in a typical batch scheduling approach, you need to extract and load the data from all the sources typically at the same cadence. It can be done once an hour, once daily, or at any suitable time frame required by the user. Though it does help in keeping things as simple as possible, it also creates a disadvantage for the user to miss out on any data or events created in between those runs which could potentially be valuable and insightful for the work to be done and for quick decisions to be made.
A common example of understanding such a situation could be a real-time tracking report. In such a situation, the timing of tracking is critical and holds much importance to the report. Waiting an hour for the next update isn’t the most ideal situation to assess the data. But with a stream, you could theoretically capture every tracking update as it occurs. Managing and being able to analyze the data efficiently is a big challenge that the user can face in such situations and becomes crucial for live video stream processing to be included in terms of providing intelligent analytics and object detection on a real-time basis.
Computer vision (CV) system libraries are typically used to discover or stream such objects of data consistency. Many tasks like parallel and on-demand processing of large-scale video streams or even extracting a different set of information from a video frame drive the use of big-data technologies in video stream analytics. They help in analyzing the data with different machine learning libraries and supporting the data in piping the analyzed data to different components of the application for further processing. This is when the CV library collects and processes data at the same time and so a server failure could detect a node failure and switch between the process to another node which may result in fragmented data and unfurnished formats.
Approach toward Data Processing
A certain approach to processing data needs to be followed to reliably handle and efficiently process large-scale video stream data. The process usually requires a scalable and fault-tolerant distributed system that is designed from principles of data analytics combined with an algorithm to race between motion detection and data buffer simultaneously. Stream processing is most often applied to data upon which some action should immediately occur. Processing delays that are detrimental to the experience of service providers and users are taken care of with the help of this technology. The data is usually processed through an IoT system that consists of web embedded smart devices that work to collect, send and act on the data they acquire from the sensor or devices. The data from these sensors are sent to the cloud using connectivity and thereafter, processed. Special software is used to process the data automatically without user intrusion.
In the first phase, video footage, which can also be considered as a stream of frames with changing content is processed in motion frameworks that can be re-purposed to express, evaluate and compute complex situations modified in video streams. It works towards extending the stream processing framework for building an expressive and rational representation which will be later used to pose continuous queries. By using various Image processing and Computer Vision techniques, pre-processing of video is done to identify the new operators. Investigation over window frames and real-time data exploration is done to compute the interaction per phase on the object and its feature vectors.
During the second phase of data processing, an amalgamation of frames per phase is expressed using a combination of continuous queries that generates an interesting event of object computation and clarity. New optimization techniques need to be optimized to infer the expressiveness of the queries and ensure the subtle object check post for the connected events. In this phase, a video stream collector works with a cluster of IP cameras to read the feed from each camera and convert the video to distinguish the collector from the stream. It also maintains the mapping of camera ID and URL that can have comma-separated lists of cameras with different specifications such as the codec, resolution, or frames per second.
Are the Video Stream Processing Services Limited to Big Tech Companies?
The answer is “No”; the technology of video stream processing is out there to be used by anyone and can be spun down to fit pretty much any sized architecture therefore it is not only guided for big tech companies as the popular opinion of people might be. Any small company or any individual can set up their database and function the program successfully using the right websites and operations. If your work is such that it requires real-time data to process and carry out tasks, this is exactly the thing to be done to formulate stronger results in less time. But if the data requirements are such that they can stall or wait for some time, then this process might be a little burdensome given the facilities required.