Security cameras may reduce crime and bad behavior when they are noticed. The problem is that they are passive. At best, they can create a record. Computer Vision can change all that. One of the largest changes that has been made, for better or worse brought on by facial recognition and biometrics. Facial recognition uses what is known as key point annotation. It also has advanced a great deal so that artificial intelligence can recognize human emotions by facial expressions.
Computer vision for surveillance and security should go beyond facial recognition. It should also detect the presence of a mask and maybe even what kind. There is a difference between the kinds of masks a person may wear for public health or compliance reasons and something like a classic black ski mask. The ski mask is more likely to indicate some malicious intentions.
Going beyond faces and masks, computer vision used for security surveillance should be able to detect people. Gait recognition is also helpful because it turns out how people carry themselves and how they move can be used to identify them. Facial recognition and even gait recognition are nothing new. Now we can go even further than those and distinguish between legitimate and suspicious behaviors in many situations too.
There are many additional features and goals to consider and support, like object recognition. That is needed to detect weapons like firearms and other contraband. When backed by an AI with these features using mighty computer vision, a security camera can be much more than a means of passive observation. The camera can do more than create a recording.
Namely, an AI-capable, a networked security camera can send alerts when it notices a banned person or someone with an active warrant. It can notify the proper authorities when it sees a gun or suspicious behavior. It is well known that facial recognition and biometrics can be used to unlock a phone, prevent fraud, and control access to secure areas too. Of course, all of this is much more complicated than the simple CCTV security cameras of yesteryear. Obviously, using a high-resolution camera helps a lot.
Now probably the parts you care about are the features of the AI and CV used in security surveillance and how to make and train an AI that has all of them. Maybe you want to create something with even more features or something entirely new that changes everything again.
To do that, you will need a large dataset of relevant pictures, especially videos. You will also need all of it accurately annotated. Finally, you'll want all of it annotated with the right tools and techniques. That way, it will provide everything needed to support the cool CV features you want your amazing security camera AI to have. This list will surely come in handy.
The Best Video Annotation Tools to Use for Security Surveillance Camera Applications
- Bounding Boxes - These are cheap and standard, so you should have them and use them.
- Key Point Annotation -This is very much needed for facial recognition.
- Cuboids -These are often better than bounding boxes and provide information for all three dimensions of a person or object.
- Skeleton Annotation -This supports features like gait recognition.
- Semantic Segmentation -This kind of annotation labels and classifies everything and every pixel of an image.
- Custom Annotation - If you are innovating some game-changing next gen feature, you may well need custom annotation.
Video Annotation Tools and Services for Computer Vision and the Security Sector
The best video annotation tools for your dataset and data annotation project are the ones that you don't have to worry about using. You can cast off the burdens of data annotation and leave it all to us instead. Key point annotation can be time-consuming work, and it is all extremely detail oriented. You will need annotation that is continuous through all the frames of a video. That is important for a number of reasons. One reason is that is how you get the data necessary to recognize and flag suspicious behaviors accurately.
Managing a data annotation project without the experience needed is a cumbersome challenge. It is also a large distraction from the rest of the development work. Data annotation takes much more time, effort, and money to do in-house than you might think. Really you want to stay focused on your core mission and company functions instead of taking on more that may be outside your scope.
We can supply all of the data, data collection, and data annotation you need or the tools for your team to do it in-house. Of course, if you already have data, you can always use more for better machine learning and deep learning results. We use all of the tools listed above to support the kinds of computer vision features and applications you need for a wide variety of use cases.
We have security in mind too. After all, when dealing with videos of private citizens and images of people's faces, there are privacy concerns. Data protection processes need to be in place, and there are rules to follow. The security of your data and your project is critical too.
Good partnerships with other companies are essential to the success of any AI company, including those in the security sector. Partnership and outsourcing can provide a vital competitive advantage. Exactly what you need when going up against the largest players. You want to secure the future of your project and company too.