Image Source: Nvidia
Video surveillance has been a prominent feature in a large portion of security set ups for decades now. Over the years, video surveillance has taken many forms. Traditional closed circuit television (CCTV) recorded video footage to video tapes that could then be played back using a video player. Then came DVRs and digital video recording and with them new ways in which footage could be stored digitally as files on a computer, rather than physical tapes. More recent iterations include IP cameras that are able to stream video over a network and can be remotely accessed and stored.
Impact of Artificial Neural Networks on Video Surveillance
Artificial neural networks (ANNs) are large networks of computing nodes that are designed to emulate the way human and animal brains work. These systems are capable of learning through experience and experimentation such as previously experienced events or those it predicts or estimates in “what if” scenarios. Usually, most IP video footage is streamed to a data center that will then archive it or actively monitor it. This footage could instead be run through one or several artificial neural networks that could then monitor it for anomalies, out of character behavior or suspicious objects or events.
Artificial neural networks could also be used in conjunction with other artificial neural networks to enable the provision of “second opinions” from other ANNs who may have different learning paths and are therefore able to provide different outlooks and solutions for individual problems based on their previous experience and predictive abilities. Using artificial neural networks instead of human video analysts could both improve efficiency and accuracy and also provide an enhanced perception of threats.
AI enabled Video Surveillance Enhancements
We will discuss below technologies that are enabled by Artificial Intelligence and are set to shape the video surveillance of today and tomorrow:
5-Big Data and Automation
Facial recognition systems have been around for a few years now and have seen varying degrees of success across various industries and applications. Using AI technologies to enhance facial recognition would seem to be the next logical step in the evolution of that technology as AI would allow for fast and accurate detection in situations such as poor lighting or obstructed targets where traditional facial recognition systems would fail. Artificial neural networks can also exchange information about a potential target in order to build a complete profile of the suspect and enable easier identification and tracking.
Facial recognition isn’t the only tracking and identification ability of artificial intelligence technologies, there is also now faceless recognition. Using similar methods to those described above, alternative information about the subject is logged such as their profile, height, build, clothing and postures and this information is then used to locate persons matching these features. Faceless recognition systems would prove invaluable in situations such as the 2016 Berlin terrorist attacks where the attacker was caught on camera various times from angles where traditional biometric facial recognition systems failed.
One of the ways in which human security video analysts spot suspicious activity is by looking for environmental factors that are out of place or suspicious behavior from personnel. When compared to early machine vision systems, human analysts were capable of detecting much more complex threats than machines and without the need to be programmed to detect them. However, with artificial intelligence this could all be set to change. Artificial intelligence systems with the capability to learn and experiment with different simulations could take in a much larger amount of information than a human and be able to process it and simulated possible scenarios in order to work out optimal ways to deal with different types of threats.
Human analysts can do this too, but in a much slower time period and would need to be trained in the various different methods and techniques used. With AI, it would have taught itself how to react to each scenario and would have the processing power to do the workload of several human video analysts. This would obviously revolutionize video monitoring and threat detection in video surveillance systems and could path the way for even more advanced AI surveillance systems that can gather millions of data points about a specific environment and detect minute changes within it.
Another huge benefit to using artificial intelligence in video surveillance systems is its ability to cross reference data with other neural networks or databases in order to fill the gaps in its own knowledge. This has already been demonstrated at Google with two Google Brain ANNs being used to enhance low-resolution images. The first artificial neural network is used to map the lower resolution image to a multitude of higher resolution images in order to understand the basic layout the image should have. The second artificial neural network would then enhance the details of the image in accordance to the layout provided in order to make it more feasible as an image.
This kind of Blade Runner-esque image enhancing technology obviously has a huge amount of potential in video surveillance and security, from criminal and terrorist identification and tracking to staff monitoring and security access operations. This kind of technology could also allow remote IP cameras to stream enhanced video across a network that could then, in the case of a crime scene investigation, be remotely accessed by a forensics expert to assist in the investigation. The same could be true for security experts in video surveillance use cases whereby detectives can access image-enhanced video to investigate potential video recordings of wanted suspects.
5-Big Data And Automation
One of the biggest benefits to video security that comes from artificial intelligence is its ability to interpret the huge volumes of data gathered by Internet of Things devices such a IP cameras and motion detectors. This capability benefits several areas and has far reaching effects on video surveillance as a whole. Previously mentioned benefits such as image enhancement, facial recognition and environmental awareness would all rely on large amounts of data in order to be as accurate and as efficient as possible. Artificial neural networks like the ones described above could be used as “filters” for the data collected by IoT devices that would aim to provide deeper insights in to the information being collected as well as learning from it.
Big data and artificial intelligence could also play a large role in automation. AI systems could interpret data coming in via IP cameras and use it to automatically trigger alarms or notify security guards if it noticed something suspicious or out of place in the video data. Archive video footage could also be analyzed by AI systems using deep learning algorithms in order to place potentially new suspects at the scene of a crime automatically, with human agents merely updating a database the AI system had access to.
In fact, AI and video surveillance have the potential to completely change how security and surveillance and even police and military work is done. AI-controlled drones could be invaluable in saving human lives when used in war zones and with more and more police departments insisting on officers wearing body cameras, how long could it be until we see AI “partners” working alongside police officers and providing relevant data in real-time based on an officer’s whereabouts or assignments. This may sound somewhat futuristic now, but the artificial intelligence technologies described above all exist and are in use today. There are even some working in the AI field that estimate we will be very close to building a general AI by the year 2045.