How Deep Learning Enhances Accuracy in Security & Surveillance Systems

Category: 

Deep Learning

The power of Artificial Intelligence has breached the threshold of usability in real-life applications and computing power is at such a level where such technologies can be incorporated in specialized devices such as security and surveillance systems.

Deep Learning as technology has its origins in imitating the way a human mind reacts to situations. Since our brain is very complex network of neurons, it can think, analyse and create different patterns and scenarios that are related to real-life events. These neural networks collect information and lead to action that is synchronises with the requirement of the particular situation.

We have already seen applications of deep learning technology in speech recognition, computer vision, voice translation and more. In fact, the results are so encouraging that the technology seems to have surpassed human capabilities in areas of facial verification and image classification. Therefore, there are veritable uses of the technology in the field of video surveillance in the security domain.

One of the great benefits of Deep Learning as a technology is the ability to accurately distinguish humans from animals. This makes for a great addition to the security arsenal of agencies where false alarms account for a whopping 95% of all security related alerts.

 

The difference as compared to other algorithms

Deep Learning technology is different from previously used algorithms in AI as it has a much deeper structure and the number of layers involved in analysis can reach more than 100 in number, thus enabling it to process humungous amounts of data. Just as humans learn through experiences and observations, the layer-by-layer abstraction process adds more value to the way scenarios are analysed and results obtained. The higher the number of layers, the more learnings that are obtained from the target object. However, the entire processing of the information is done across these layers by computers and there is no manual intervention required. This way, more information is extracted from the target, including those of features that are impossible to describe. Deep Learning enables better than human-eye pattern recognition and even brings in those features that are not recognized by the human eye.

 

Why it is better than existing security devices

It has been observed that conventional security systems and devices only detect moving objects without too much of an analysis. Even the smart IP-based cameras can only map individual points on a shape, slowly and one after ther other, making it increasingly difficult to calibrate certain physical features such as forehead or cheek, thus decreasing the accuracy of the image created. In case of perimeter security, while there are technologies that provide comprehensive security, they still have their shortcomings. For instance, infrared emission detectors can be crossed over but can also generate false alarms due to animal bypassing them. Likewise, electronic fences are a safety hazard and their use is only limited to certain specific areas. Majorly, some of these solutions are quite expensive and even complicated to install. Since objects such as animals, plants and even light can case false alarms, the ability to accurately detect human beings improves the efficacy of perimeter VCA functions.

 

Frequent false alarms have long been the bane of intelligent security solutions. According to various surveys, nearly two man hours are lost each night just to verify the authenticity of an alarm. Deep Learning systems alleviate this problem to a large extent with incidents of false alarms going down by 38% through the use of security systems that have Deep Learning technologies built-in. This reduces the incidents of false alarms by atleast half, thus positional Deep Learning as a key enabler of perimeter security, with more accurate line crossing, intrusion, entrance and exit detection.

The power of Artificial Intelligence has breached the threshold of usability in real-life applications and computing power is at such a level where such technologies can be incorporated in specialized devices such as security and surveillance systems.

Deep Learning as technology has its origins in imitating the way a human mind reacts to situations. Since our brain is very complex network of neurons, it can think, analyse and create different patterns and scenarios that are related to real-life events. These neural networks collect information and lead to action that is synchronises with the requirement of the particular situation.

We have already seen applications of deep learning technology in speech recognition, computer vision, voice translation and more. In fact, the results are so encouraging that the technology seems to have surpassed human capabilities in areas of facial verification and image classification. Therefore, there are veritable uses of the technology in the field of video surveillance in the security domain.

One of the great benefits of Deep Learning as a technology is the ability to accurately distinguish humans from animals. This makes for a great addition to the security arsenal of agencies where false alarms account for a whopping 95% of all security related alerts.

 

The difference as compared to other algorithms

Deep Learning technology is different from previously used algorithms in AI as it has a much deeper structure and the number of layers involved in analysis can reach more than 100 in number, thus enabling it to process humungous amounts of data. Just as humans learn through experiences and observations, the layer-by-layer abstraction process adds more value to the way scenarios are analysed and results obtained. The higher the number of layers, the more learnings that are obtained from the target object. However, the entire processing of the information is done across these layers by computers and there is no manual intervention required. This way, more information is extracted from the target, including those of features that are impossible to describe. Deep Learning enables better than human-eye pattern recognition and even brings in those features that are not recognized by the human eye.

 

Why it is better than existing security devices

It has been observed that conventional security systems and devices only detect moving objects without too much of an analysis. Even the smart IP-based cameras can only map individual points on a shape, slowly and one after ther other, making it increasingly difficult to calibrate certain physical features such as forehead or cheek, thus decreasing the accuracy of the image created. In case of perimeter security, while there are technologies that provide comprehensive security, they still have their shortcomings. For instance, infrared emission detectors can be crossed over but can also generate false alarms due to animal bypassing them. Likewise, electronic fences are a safety hazard and their use is only limited to certain specific areas. Majorly, some of these solutions are quite expensive and even complicated to install. Since objects such as animals, plants and even light can case false alarms, the ability to accurately detect human beings improves the efficacy of perimeter VCA functions.

 

Frequent false alarms have long been the bane of intelligent security solutions. According to various surveys, nearly two man hours are lost each night just to verify the authenticity of an alarm. Deep Learning systems alleviate this problem to a large extent with incidents of false alarms going down by 38% through the use of security systems that have Deep Learning technologies built-in. This reduces the incidents of false alarms by atleast half, thus positional Deep Learning as a key enabler of perimeter security, with more accurate line crossing, intrusion, entrance and exit detection.