Gun Detection

Gun Detection, Tracking and Reconnaissance

Gun Detection - The world has become much more connected and much more dangerous. This has resulted in an increased awareness, security cameras are now everywhere, UAS, robots and smart sensors with camera payloads are also being used to secure facilities, perimeters, events and infrastructure.

Cameras must be monitored with human eyes, this is costly and subject to human error.  The increase of senseless violence in America, especially within the education system, giving any camera even one on a UAS, the ability to recognize an object like gun is like creating a new super hero.

Nobody has a solution that can eliminate the possibility of an attack like this, but Gun Detection, could give people an early warning about impending danger. It could also provide on-site security teams, first responders and police with situational awareness.

Smart gun detection that works as an early warning integrated with our UAS based active shooter response provides the best opportunity to stop a horrific incident before the first shots are fired. Gun detection can be a powerful deterrent to the threat of violence.

In today’s world that is important for schools, places of worship, public venues as well as in hospitality. Give your security the ability to target a shooter approaching your facility, spot a shooter in a crowd of people, search for a shooter from the air or access a dangerous situation indoors or outdoors, while keeping your team far from harms way.

Gun Detection Technology

This technology should not be your only security measure to stop active shooters, however, the ability to have existing security cameras, robots or hovering UAS detect guns is very powerful. We provide a technology that integrates cameras, smart sensors, software, AI and UAS into a system that delivers early visual gun detection, shot detection, alerts, mass notification, static, mobile or aerial shooter tracking and reconnaissance.

Now with the prospect of armed teachers becoming a reality, gun detection should be an obvious part of the school security discussion. If any unauthorized person with a gun is on-site at a school, there must be an alarm and alert system capable of informing the proper authorities.

Teachers with guns are an absolute last resort for protecting our nation’s students. By giving onsite security, police and first responders better situational awareness, gun detection and shooter tracking can help save valuable time.

Namely, by using your existing security cameras, Epiphany gun detection video analytics technology will enhance your ability to stop or deter an active shooter. Consequently, this can provide onsite security and first responders with more time and information about how to secure a location.

Gun Detection

A Significant Part of Active Shooter Defense

Gun detection is an optional part of our Active Shooter Response solution, for administrators and SRO’s (Security Resource officers) who are on-site, a mobile application is available. 911 dispatch can utilize the web-based application to support law enforcement and first responders.

At the center of Epiphany Gun Detection is a proprietary artificial intelligence platform that is trained to identify guns. Used with existing or new security cameras, your system is enabled to detect when and where guns are visible. This allows for sending real-time alerts and updates to the appropriate authorities.

If a threat moves throughout a facility, Epiphany Gun Detection will detect their weapon on each camera wherever they are within view. An alert is generated each time a gun is detected, allowing the necessary school security personnel to understand where they are moving. As a result, this provides much needed real-time situational awareness.

Our algorithm is constantly learning to better detect guns. Our staff, highly trained former American war fighters, provide archived analytics, best-in-class data to make the detection of guns as effective as possible. Our video analytics technology continuously learns how to detect different types of guns, improves our capability to respond quicker.

Intelligent Video Analytics

Epiphany Gun Detection is an Intelligent Video Analytics algorithm, powered by AI technology that can detect guns and recognize faces in real time. Once a gun or persons of interest are in view of camera, our platform sends alerts that lock can doors and provide emergency personnel with time sensitive information regarding an active shooter’s description and location.

We provide school administrators and decision makers with a simple, intuitive situational awareness platform that gives first responders a powerful tactical advantage. Our technology identifies faces and objects in video. Frame by frame, it records the x, y coordinates of its findings and displays a bounding box around the found face or object.

As an implementation of Recognition Technology, our software learns to recognize a face or object using an initial training set of sample images. As it analyzes this training set, it computes factors that are likely to make the face or object unique and uses these factors to create a learning profile of the item for future recognition.

Gun Detection

High Degree of Accuracy

To improve its accuracy, our solution also uses Tracking Technology and User Assistance where the user has the option to correct mistakes.

The software tracks each item it finds in the video. It uses the findings in one frame to identify faces or objects in the next and previous frames even if the object's appearance changes slightly from frame to frame. For example, when a person turns his head or smiles.

When the initial, automated detection completes, the user has the option to confirm the findings. If any errors are found, the user can correct them with an easy-to-use interface.

The results of Tracking and User Assistance allow the system to update its learning profile for future use. By combining Recognition Technology, Tracking Technology, and User Assistance, our solutions identify and track faces and objects in video with a very high degree of accuracy over what Recognition Technology can accomplish alone.

Gun Detection

Traditional Image Processing Approach

This approach is most appropriate if the object you want to identify has specific, distinctive attributes that are easily identified. Also, the object should be distinct from the background.

For example, identifying a part in a metal shop based on its shape and the location of holes for screws. Or identifying a PC board using the placement of components as well as their shapes and other patterns.

Or identifying Lego pieces using their colors, shapes, and the number of rows and columns of embossed circles present. This approach uses predefined rules to classify an object into one group or another.

The parameters used by these rules are fixed and do not change which means the algorithm does not "learn" from previous experience for use when it encounters new images.

Gun Detection

Epiphany Gun Detection - The AI or Machine Learning Approach

This approach is most appropriate when the variations in the visual appearance of an object are diverse (even after camera perspective corrections are performed) and many sample images is available to learn from. For example, recognition of specific faces, plants and animals where a very high number of parameters are used to create a complete and a definitive identification.

The sample images used for learning need to be representative of both the object and the environment in which the object will be recognized.

This approach is based mainly on statistics. For this reason, the choice of samples used for learning will have a major impact on its accuracy.

Gun Detection

Epiphany Gun Detection - Our Hybrid AI Approach

When aspects of both approaches come into play, a hybrid approach is called for.

For example, when an image processing algorithm updates the parameters from its rules, according to new sets of images, it adapts to new data by learning. Another example is that of a machine learning algorithm that combines multiple features extracted using various image processing techniques to produce the best result.

The starting point for designing algorithms for gun detection was a requirement analysis. We analyzed publicly-available CCTV recordings featuring crimes committed using a dangerous object.

Several observations were made:

Real-life CCTV recordings or UAS video are sometimes of poor quality, suffering from blurriness, under- and over-exposure, motion, compression artifacts, poor quality and other factors. The dangerous object is sometimes visible only for a limited period in a scene, remaining hidden by the perpetrator most of the time.

Based on these observations, we have created a set of requirements for Epiphany Gun Detection. First, we decided that our algorithm needs to cope well with poor quality input.

Our Hybrid AI Approach Continued

This means a low-resolution input image, still aerial shots, and a small size images of guns. We also decided that the algorithm should work in real time utilizing no more than a typical desktop computer and without the need for specialized hardware, such as access to a supercomputing center or parallel computing.

One of the most important points is to keep the number of false alarms as low as possible (high specificity), even at the cost of missing some events (at the cost of sensitivity). Since an automated algorithm generates too many alarms, the operator starts to ignore them, which, in turn, renders the whole system useless.

Moreover, an algorithm that misses some events in the beginning, but learns from its mistakes, we could imagine just a few years ago. False alarms are unacceptable in practical application they can generate high costs, especially if each alarm must be verified by a human.

This is why we’ve integrated automated technologies like robot and UAS verification. Our goal is to maintain a low number of false alarms, we try to achieve as high a sensitivity as possible.

Finally, following discussions with CCTV system and UAS payload manufacturers, we have designed the system to be a sensing and supporting system, rather than a decision making one. This means that each time a dangerous object is detected by either a static camera or by a UAS, the onsite security personnel must be alerted in order to assess the situation and take appropriate action. Since such automated