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 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 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.