Businesses often need human intervention for many things, where sometimes it can be automated in one way or the other. Like in a manufacturing business, they often need human intervention to monitor the machines. Here Computer vision systems can analyze visual information to predict machine downtimes or disruption among shop floor employees.
Moreover, they can monitor the production line to spot the defects and alert the administrators to take action instantly as the defect is detected. Insurance companies often face problems in verifying the authenticity of insurance claims. Computer vision (CV) can assist in analyzing images to identify valid claims and forward them to the right person. Similarly, there are many cases where there is a need to analyze what is happening in the video or image but without human intervention, so here comes the role of vision and analytics.
The process of examining digital image/video signals to understand the physical world using the latest technology instead of the human eye is known as visual analytics.
One can find insights from any available data, i.e., images or videos, using vision analytics. It helps to recognize and predict events based on already existing data. Vision analytics can also be referred to as video/image content analysis or intelligent video/image analytics.
Why do we need vision analytics?
Vision analytics have become an hour-long necessity as technology evolves, providing a variety of applications for security, monitoring, and movement sensation. Video analysis has been established as a solution to assist those who provide critical infrastructure security with a powerful way of identifying intruders, tracking people or objects, and generating alarms on behaviors. In manufacturing, identifying macro and micro-level defects in the production line can be done more efficiently. Also, real-time monitoring of plant growth can be done, and we can detect changes in crops due to disease or malnutrition. There is a need for vision analytics as -
It provides solutions to many real-world problems that involve using computer vision by rendering analytics insights.
A few specific use cases are:
-It makes the surveillance system more efficient.
-Reduces workload for security personnel and managers
-It helps capture the full amount of security video by making the IP camera system very smart in its work.
Let’s deep dive into the general working of video analytics working flow. The below diagram gives the high-level architecture for the same:
When the video is provided for vision analytics, it is recognized as a series of frames consisting of pixels. Some helpful information is extracted from those frames based on the requirement, and then the analysis is done on it.
Video analytics can be done using Machine learning and AI, it will compare the pixels of each frame, and the software will capture every slight change, and, as viewers, we view it in a video stream as motion.
Movement Detection is the basis for complex analysis. A well-trained model can detect patterns of pixel changes and translate them.
This method allows us to detect if an object is moving, missing, or added. Moreover, video analytics can recognize the object to track it among various cameras.
Services that can be used to do live video analysis like shot detection, object detection, face recognition, label detection, etc. are:
Some most important features of visual analytics are:
Video Motion Analysis
Video motion analysis is used to obtain information about moving objects in video. Examples of this include mobility analysis, Sports repetition, speed and acceleration calculations, and performance analysis in a team or individual game. We can also estimate the velocity of any object in the camera or the video itself.
It triggers an alarm when a stationary object, such as a piece of art, is removed from the video or selected stream of a video. Any motion is allowed in the protected zone, and it will trigger the alarm only when the object is removed from the scene. This analytical behavior allows users to define an object or place they like in the forum.
As the industry is shifting from standard products to personalized products based on customer requirements, 3D smart cameras will rule the industries in the coming years. 3D photography also helps manage the automatic navigation objects by object acquisition, localization, etc.
The process uses heat radiation and infrared rays to detect objects in the light/dark background. There are thermal cameras that can identify the difference in temperatures of warmer/colder objects/ beings to detect them and work on them. Using this, it becomes easy to identify the presence of a person or an animal against the cold and dark background. When this is used with vision analytics, it sends the temperature range alerts. This is especially useful for security purposes. The percentage of false security alerts can be reduced, thereby improving the efficiency of the security system.
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What are the challenges of Vision Analytics?
Automatic computer detection is not yet reliable enough to detect confusing or tracking items. It hasn't matured to an extent where it is straightforward to apply it to real problems. Therefore, analyzing in such cases may get difficult.
Establishing a machine that can think is a tricky task, not just because it is difficult for computers to do, but because we are not entirely sure how human vision works in the first place and then analyzing that work could be more challenging.
For analyzing video/image content, one should know about data analysis, data exploration, visualization, and some statistics. All these skills are difficult to find in one person.
Although, like any other research involving the brain, it is still a long way off to cover for proper vision and analysis.
Use Cases of Vision Analytics
The use cases of Vision Analytics are listed below:
Revolutionize Customer Analytics
Customer analytics refers to organizations capturing and analyzing customer information to make important business decisions. For example: By analyzing customer interests from vision analytics, businesses can decide which things they should invest more in and vice versa.
Medical diagnosis relies heavily on imaging, scanning, and analyzing them. Ultrasound imaging, MRI, and CT scans are part of many modern medical technologies. Computer diagnostic technology promises to simplify the procedure and prevent false positives and reduce treatment costs. Moreover, it can be used to analyze X-rays, blood loss measurement, pose estimation, etc.
Pedestrian Detection: Detecting pedestrians and taking into account variations related to body composition and condition, closure, light in different situations, and background overlap.
Parking Occupancy: Computer vision is already widely used to locate visual parking in Parking Guidance and Information (PGI) systems and analyze which time of the day parking is less occupied and vice versa.
Traffic flow Analysis: Algorithms can now accurately track and calculate highway traffic or monitor and analyze traffic congestion in urban areas to help design better traffic control systems and improve road safety.
Road Condition Monitoring: This can be used to analyze the road conditions to improve them.
Self-driving Cars:Self-driving cars collect environmental data from sensors and cameras and interpret and respond appropriately.
It can be used in defect inspection. Camera-based systems can collect real-time data and use computer vision and machine learning algorithms to analyze and measure results against a predefined set of quality standards.
In the agricultural field, vision analytics can be used in recognizing, classifying, and counting the insects that are harming the crops. Computer vision systems can process images and obtain the vegetation index (VI) to accurately estimate the soil water balance. This allows farmers for more effective irrigation planning.
As vision technology continues to grow, the time is not far off when the vision industry will take the lead as the real solution to real-world problems. Whether it is road signage and road transport signal acquisition, advanced aerial photography in the field of defense, or detection-based detection in production lines, computer vision has a lot to offer across the industry. Traditionally, the increasing security threats and the need for improved surveillance have exacerbated the demand in the video analytics market. Recent progress in AI and ML, big data, computer peripherals, and specialized computer software platforms have increased the acceptance of powerful AI video analysis to go beyond providing basic security and surveillance.
Technology, of course, should be used to improve business processes, increase automation, strengthen security and effectively manage traffic. The increasing demand for real-time automation drives new ways of analyzing videos. To meet these needs, video statistics must respond quickly, requiring real-time operation on local computer systems. The computer vision industry is advancing in light-weight neural networks that are remarkably accurate. This enables us to rethink what a comprehensive IoT sensor can do beyond traditional security to provide operational and business intelligence.