Abstract: A threat detection system and method for detecting panic detection in a crowd of people. The threat detection system using software algorithms detect people's movement from video camera scene and video feeds. The system analyses the recent history of the scene to describe features that would be considered common/normal in the scene when enough people are present. The system then uses that baseline information to continually analyze frames with the requisite number of people in a frame and update the baseline features. If the features of the scene change dramatically based on the perceived movement of the people in the scene and meet or exceed the threshold features for movement in enough consecutive frames, then the systems determines that there is panic in the scene.

Abstract: A system and method for utilizing a multi-sensor video surveillance system for traffic and compliance reporting. Utilizing closed caption televisions (CCTVs), cameras and other sensors, the system can generate heat maps and monitor activity in the scene. Based on the heat maps and object detection, the system can monitor traffic pattern for vehicles and monitor people for personal protective equipment (PPE) compliance.

Abstract: A multi-lane elevated body temperature preventative scanning solution using GigE vision and tracking. This multi-lane solution is used to scan and track movement of people through an elevated body temperature scanning solution. When a person enters the frame, their identity (ID) is tracked within the field of view of the thermal camera in a designated scanning area from there it is monitored through the entry and exit of a premise to acquire a preventative temperature screening. Multiple machine learning and tracking routines are used together in this innovative offering.

Abstract: A system and method for a smart stand-alone multi-sensor gateway for detection of person-borne threats. The multi-sensor gateway system consists of two pillars comprising a plurality of sensors that build a gateway for patrons to pass through that detects concealed threats carried on-body. The threat detection relies on artificial intelligence (Al) to analyze the sensors’ data and assess the presence of a threat. The Al is performed on an edge device contained within the gateway. The gateway can consist of a plurality of peripherals that enhance the sensing capability of the gateway, such as a camera or an accelerometer, and provide the security guard with information around alerts and threat locations such as with displays or audible alerts and manage the operations such as displays to control throughput.

Abstract: A system and method of automated logistical vehicle registration and validation for remote monitoring. Cameras are deployed at the gate to a trucking logistics facility to record detailed information (e.g., license plate, identification info) on vehicles. Alternatively, vehicle and drive identification credentials can be provided by a mobile application from the driver. The cameras are connected to a data center hosting the automated logistical vehicle registration and validation system. Cameras are placed around the entry gate of a facility that takes snapshots of vehicles “checking in”. The system takes this info with assistance from artificial algorithms to read data and images (e.g., photos) to compare and verify the info. Recommendations are then sent to a guard to approve entry of the vehicle.

Abstract: A threat detection system and method for detecting panic detection in a crowd of people. The threat detection system using software algorithms detect people's movement from video camera scene and video feeds. The system analyses the recent history of the scene to describe features that would be considered common/normal in the scene when enough people are present. The system then uses that baseline information to continually analyze frames with the requisite number of people in a frame and update the baseline features. If the features of the scene change dramatically based on the perceived movement of the people in the scene and meet or exceed the threshold features for movement in enough consecutive frames, then the systems determines that there is panic in the scene.

Abstract: A system and method of a threat detection system to automatically disseminate information being communicated over a communications channel during an event to help coordinate resources from the command center. By using speech processing algorithms along with artificial intelligence and natural language processing (NLP), a threat detection system can pull out key insights from the radio communication between the security personnel. Text in the radio frequencies can be recorded and transcribed and actionable alerts can be created.

Abstract: A system and method for utilizing a multi-sensor video surveillance system for traffic and compliance reporting. Utilizing closed caption televisions (CCTVs), cameras and other sensors, the system can generate heat maps and monitor activity in the scene. Based on the heat maps and object detection, the system can monitor traffic pattern for vehicles and monitor people for personal protective equipment (PPE) compliance.

Abstract: A system and method of at using all CCTV cameras simultaneously to find any person of interest in real time and alert security to their location. The person of interest may be manually selected by the user or automatically by computer software and algorithms. In a preferred embodiment, security officers or threat detection system users (i.e., security team) confirms they want to track perpetrator or people in a video feed scene. The user would select these person(s) of interest whereby the system is triggered to begin tracking the person(s) of interest. The system will then present the feeds of the location of the person of interest is located in, in order to allow the security team to track and catch the person(s) of interest.

Abstract: A system and method for more accurate recognition of adherence to social distancing regulations in image data. A curated dataset of surveillance footage is used that shows people at a variety of angles at a wide variety of distances. The videos of this dataset are annotated and a second, numeric dataset is created as input into various classical machine learning models. The trained model is tested on more annotated data and shows a noticeable improvement over the other attempted methods. The resulting analytics allows for accurate distinction between pairs of people that are at least 6 feet apart and pairs of people that are not. This method utilizes a random forest machine learning model to improve on the Euclidean method that measures distance between human detection centroids in the 2-dimensional target image.

Abstract: A multi-sensor threat detection system and method for elevated temperature detection using commodity-based thermal cameras and mask wearing compliance using optical cameras. The proposed method does not rely on the accuracy of thermal cameras, but the combination of mathematics, statistics, machine learning, artificial intelligence, computer vision and Manifold learning to construct a classifier, or set of classifiers, that are able to, either alone or working as an ensemble, evaluate a person as being ‘normal temperature’ or ‘elevated temperature’ by virtue of ‘how they present to the camera’ vs. any absolute temperature measurements from the camera itself.

Abstract: Embodiments described herein relate to a threat detection system that shows a user an incident as it develops in real time by leveraging artificial intelligence (AI) to more accurately focus cameras and highlight the areas of concern within those feeds, providing a much more efficient user interface to the operator. These annotated feeds and feed focused triggering events can also be connected to third party systems. This timeline of events and evidence (small annotated clip of detection when available) is archived and can be reviewed at a later date, containing an accurate timeline of the incident as it progressed.