Introduction
We need to embrace the concept of computer vision in homes rather than shy away from the idea of exchanging personal data to achieve new levels of protection, safety, comfort, and entertainment. Computer vision combined with NLP and ML enables computers/systems via digital images or video to understand what they see.
When systems can detect and recognize objects, according to what they are scheduled to do, they can deliver intelligent behavior. Automotive space is one area that has successfully demonstrated how computer vision can change our lives. Car systems that use computer vision can recognize the driver behind the wheel and can warn the driver when he starts to swerve out of his lane to see the surrounding area.
Many customers on their smartphones are already using computer vision and don't even know it. To recognize facial features and position overlays (philters) in the right positions, both Snapchat and Instagram use computer vision tracking.
How does Computer Vision help us in making things secure?
Accepting computer vision into your house and connecting it to your connected devices helps your daily routine to have a new level of convenience. When you arrive and open the door for someone, the front door will be able to see or stay locked when an unknown person (face) approaches. Alarm systems are smarter, able to distinguish who are family members (including age and gender) and who are not.
If an elderly family member or visitor trips, or if a child is climbing up the stairs, on the countertop, or anywhere that puts the child in danger, indoor surveillance cameras will send a warning to your mobile, taking it a step further. Nest, Logitech, and other smart home manufacturers have either begun offering customers these smart security features as a premium subscription service or have already incorporated them into their newest devices.
Computer Vision in Intrusion Detection
Abbreviated as IDS, an Intrusion Detection system plays an important role in providing the required security assurances for all networks and information systems in the world. One of the solutions used to decrease malicious attacks is IDS. As attackers often change their attack tactics and find new methods of attack, IDS must also develop by implementing more sophisticated detection methods in response.
The enormous data growth and substantial developments in computer hardware technology have led to the existence of new studies in the field of deep learning, including intrusion detection.
To provide a high degree of security and security staff monitoring effectiveness, high-performance AI systems can make the task monitoring process automatic for high-risk sites. Also, these intrusion systems can identify objects based on size and location. However, they fail to recognize the type or form of the detected object.
Perimeter Defense (Intrusion Detection) systems with high-end artificial AI algorithms to identify a multitude of different types of objects can now discern objects of interest, thus dramatically reducing the rate of such intrusions that might indicate a false alarm. The more sophisticated systems, such as those provided at IronYun, allow its customers to design ROIs based on intrusion detected points, high-value areas, and or any other region that may be beneficial for alerts.
Similarly, the applications designed for face and license plate recognition have the ability to detect people or cars(the license plate) in addition to solutions for motion detection and use pre-designed data to identify distinct faces or plates that should be watched regularly, similar to the pre-designed lists.
Needless to say that these systems will also allow its customers to search for faces that are not provided already on the camera. For example, if a person is identified hanging outside a house many times, one can store their pictures in the designed watchlist and fix an alarm when the face is identified again around the house or in your surroundings.
The main advantage of the system is that before the troublemaker completes the act, the warnings will assist in discouraging and avoiding vandalism or robbery and inform the authorities of the scene.
Conclusion
AI-based security measures combined with computer vision, deep learning, ML, and NLP training can do all the boring work for you to help deter fraud and vandalism. They are also the most accessible security solutions available with a strong return on investment due to their low cost and outstanding reliability.
Stopping crime is a challenging, ongoing challenge, but enterprise vendors and law enforcement can do it more easily with the right AI apps. This is also one of the reasons why people are excited about an acceptable career in the AI sector.
