As companies around the world have started micromanaging; to safeguard their business operations, the implementation of security cameras has become a growing reality. However, the introduction of AI & ML capabilities in the surveillance sector makes cameras smarter. Security cameras not only monitor but can also detect objects and for that, image annotation is crucial. It empowers computer vision models to interpret and learn from image datasets to make accurate predictions. Companies are capitalizing on this phenomenon and enhancing their surveillance game immensely.
Image annotation is a process to inculcate labeled image datasets into AI & ML models to identify objects accurately. Therefore, the camera view can recognize and make decisions accordingly. It can identify objects, movements, people, animals, etc. The amalgamation of imaged annotation and security cameras is proving to be effective in preventing internal and external theft, vandalism, accidents, etc. For example, you can detect any suspicious activities in a bank by identifying the objects customers are carrying. There are many such great examples of image annotation services that are powering the security cameras to perform better. In this blog post, we will focus on that.
Data annotation can influence security cameras to identify trespassers and ensure a safe environment for your commercial properties. Let’s dig deep and find out more.
Image annotation can prove to be a catalyst for security cameras in this segment. It can prevent serious crimes by simply analyzing human behaviors and by placing them in separate bounding boxes. This process involves precise data tagging for the computer vision model to recognize.
It enables security cameras to estimate the number of people, identify demographics among others, etc. It helps to ensure public safety and helps to organize future events.
Image annotation leverages 2D or 3D bounding box techniques to detect individuals or objects from the crowd. Annotators draw a box around the individuals or objects and label them meticulously. This can differentiate individuals by their faces and identify the right person from the crowd.
The use of image annotation in security camera monitoring is a useful tool to prevent unauthorized access to restricted areas as the camera will match from the labeled datasets in real-time, then only will allow access to the authorized persons.
Adding onto the above discussion, image annotation offers an edge to security cameras to protect your commercial settings. The cameras will recognize faces as well as you have labeled datasets. Using the landmark annotation process, annotators tag each facial region and points separately.
Accurate data labeling is pivotal for security cameras to detect facial expressions, behavior, gestures and orientations at each point in motion. It can help to identify people with suspicious movements and alerts the relevant team immediately.
Today, security cameras are everywhere. The consistent tagging datasets and training deep learning models have turned these cameras into eagle-eyes. It is becoming lethal in the context of preventing crimes and making sure nothing passes by without checking and verification, not even vehicles.
Security cameras connected to well-trained computer vision models are delivering extraordinary results, especially in places where security checking for vehicles is mandatory such as airports, railway stations, shopping malls, toll plazas, hospitals, etc. It is also effective in monitoring and managing traffic in real-time on highways and crossings. Besides, the “security” part can help to detect overspeeding, traffic violations, road accidents and can immediately inform the officials.
More often than not, you will see a guy suspiciously roaming around the ATM, and the very next day, you will find out some crime has been reported in that spot. However, security cameras equipped with well-trained computer models can counter this problem for banks. Image annotation helps CCTVs to detect and track the suspect. It can be used in gender classification, prevents overcrowding, and blocks unusual activities to occur.
Predominantly, it is useful for the government sector such as military base camps, radar centers, strategic infrastructure, prisons, etc. However, it is also effective in guarding hospitals, ATMs, industrial complexes, etc.
Night vision thermal cameras can be trained on the same principle to identify objects through image annotation. Even in the darkest of hours, it can detect people, creatures, unwanted movements, etc. Precise data annotation can make your security cameras agile so that you stay protected round the clock.
To conclude, image annotation can bring the best out of security cameras. It is turning security cameras into ‘smart security cameras’. Businesses that are looking forward to protecting their interest are tapping into this amalgamation.
However, labeling datasets and inculcating the AI & ML models for accurate results is quite a difficult task. It is expensive and time-consuming. Therefore, image annotation outsourcing to an expert firm is considered a wise decision. We at Maxicus, offer accurate tagging of datasets and training of your computer vision models at an affordable price. Contact us to learn more.Categories: