In recent times, video content analysis or real-time video analytics has risen to prominence and gained attention from various industrial and academic spheres. The development of real-time video analytics can be attributed to the advancements made in the areas of deep learning, artificial intelligence, and machine learning. Video Analytics has introduced the element of automation, thereby bypassing the need for human intervention.
While the idea of real-time video analytics may sound great, but the question still remains – What is Video Analytics? How can video analytics companies enhance your business? And where can video analytics be used?
What are Video Analytics?
In simple terms, video analytics involves the use of algorithms to recognize any spatial or temporal events in video footage automatically.
The analytics software differentiates between the various object types and identifies certain actions or behaviors that may trigger an alert or a response from the user. The mining of this data could also share some key insights with the user.
Real-time video analytics tools perform real-time monitoring of events. They may capture and analyse objects, object attributes, movement patterns, or behavior.
Typical events that a video analytics solution can detect could range from identifying vehicles that are not following traffic rules, a sudden burst of smoke or flame, a suspicious individual moving discreetly, and more.
Pros and Cons of Video Analytics
Some of the key advantages of Video Analytics include:
- In-Depth Analysis of video elements, which may be missed by the human eye
- Deep analysis of video is carried out at all times with zero lag and minimum errors
- Video analytics solution help cut down labor cost
- Allows for objection recognition and motion detection
- Prompt alerts and notifications in response to trigger events
- Appreciable return on investments
On the other hand, video analytics is limited by the following:
- Involvement of heavy capital investment at the initial stage
- Requirement for large bandwidth networks
- Susceptibility to hacking and privacy breaches
Clearly, the benefits of the real-time video analytics technology outweigh the drawbacks. Further, choosing reliable video analytics companies will help in overcoming the projected downsides of video analytics.
How Does it Work?
While the architecture of a video analytics system may vary depending on the area of operation and the requirements, the core scheme remains the same.
At the core of the system, we have the following building blocks:
- System Feed
- Central or Edge Processing
- Events Database
- Review Unit
The device that captures the video data from the source forms the first component of the video analytics network. Network-connected CCTV cameras, online video feeds, and traffic cameras help collect data.
The primary factor while considering the streaming source is the coverage. The device should be placed in such a way that it offers a clear view of the area under consideration. When it comes to data, more is better. Hence, having multi-angle coverage is important.
Central or Edge Processing Unit
The processing and analysis of video data can be carried out on-site (edge processing) or at origin (central processing). In the former, the processors are located within the camera, while in the latter, it is located at a monitoring station. Depending on the processing unit, the camera designs are selected accordingly.
Hybrid solutions for processing have also emerged as a viable option, thereby combining the advantage of both the processing techniques. As a portion of data is processed on-site, the data load on the central processor is reduced drastically, which reduces bandwidth and capabilities.
To assist the video analytics solution in making automatic decisions, you need to define the scenarios that are in focus and the subsequent action. Whether it is identifying shoplifters or locating car crashes, you need to define the event and also the tasks that the system must perform.
Even though the system may operate as a standalone solution, it needs to be monitored by humans. They shall carry out additional tasks or make decisions based on alerts or notifications generated by the video analysis system.
Some of the commonest industrial applications of video analytics are in the following areas:
- Surveillance Systems and Security
- Smart Parking
- Transportation and Traffic Analysis
- Crowd Management
The Future of Video Analytics
The future of video analytics and video analytics companies only looks brighter from here onwards. The refining of artificial intelligence, machine learning, and deep learning technologies will be responsible for this upward trend in video analytics. Further, the prevalence of high computing power IP cameras and similar technological devices will allow video analytics to resolve complex issues. As the system becomes more accurate, more streamlined, and smarter, it is only slated to grow further at a healthy rate.