As IP cameras become cheaper and easier to activate, security cameras are now pervasive in homes and businesses. However, no human can digest dozens of video streams continuously.

Computer vision is considered a subfield of artificial intelligence and machine learning. It focuses on helping computers see and understand the content of digital images. Recent shifts from statistical methods to deep learning neural network methods help computer vision in developing a variety of security uses. Here are four key ways advances in computer vision are improving physical security:

  • Recognizing and labeling an object/pattern

Recognizing objects and patterns has many applications: automatic medical diagnosis (health care), defect reduction (manufacturing), pest infestation prediction (agriculture).

In security, computer vision does even better than human eyes in detecting humans, vehicles or guns.For little cost, a regular camera can accurately detect a human presence from different angles even when only part of an arm appears in the field of view.

  • Answering the age-old question: Who are you?

Authentication through facial recognition entered the mainstream through iPhone’s FaceID. The implementation of facial recognition, however, preserved privacy by storing data only on the user’s device. Lately, it’s being used for many other use cases where biometrics are stored in central databases.

There are general-purpose facial-recognition solutions offered by public cloud vendors such as Amazon, Google and Microsoft. Computer vision scientists are also developing other approaches to identify people based on their geometric shapes (height, width and body-part proportions) and gait cues (stride length and amount of arm swing).

  • Inferring actions from a sequence of images or video

Computer vision can help digitize specific events, times and locations, and can use this data to track behavior. For example, a camera in a retail store can track employee activity in real-time, alert when a new customer enters or exits the store, and track their journey. This information can not only be used to detect loitering outside the stores and reduce shoplifting, it can provide actionable insights for improving traffic flow and placing merchandise.

  • Modifying or recreating realistic images

This technique would be useful in generating visualization for critical security incidents and reconstructing faces or license plates to provide law enforcement richer information.

Computer vision can automate several tasks without the need for human intervention. As a result, it provides organizations with a number of benefits. Need for regular monitoring?

With these new techniques and rapidly improving capabilities, computer vision is progressing toward solving certain security challenges. Scientists all over the world are making rapid advances, and companies are making AI usable by providing platforms that could be used by nonspecialists.

Google’s Tensorflow is an open-source AI-software platform that anyone can use. Labeled datasets, needed for training and testing, are becoming widely available. 

Just like any other powerful technology, computer vision has both benefits and serious concerns. The rise of AI in surveillance stirs fears about loss of privacy and government intrusion. Under the guise of security and crime prevention, AI could be abused to track people and control behavior.

Conclusion

By better deriving intent from objects and motions and filtering out the false alarms, computer vision shows enormous potential as a risk reduction tool and an information filter to help security personnel be more effective at their jobs. With more active researchers in the field, we expect to see far more accurate and reliable vision in the future. The day AI can guard you while you sleep may not be that far off.

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