An apparatus includes an extract unit configured to extract features of a first image based on an electromagnetic wave in a first frequency band, an acquire unit configured to acquire motion information about the features, a classify unit configured to classify the features into a first group and a second group based on the motion information, and a remove unit configured to remove, from the first image, a signal corresponding to the feature belonging to the first group.

A method includes receiving, from a plurality of magnetic field receivers including magnetic sensors, data characterizing samples obtained by the plurality of magnetic field receivers, the samples of a combination of a first magnetic field and a second magnetic field resulting from interaction of the first magnetic field and an object; determining, using the received data, a polarizability index of the object, the polarizability index characterizing a magnetic polarizability property of the object; classifying, using the determined polarizability index, the object as threat or non-threat; and providing the classification. Related apparatus, systems, techniques, and articles are also described.

A security inspection system and a method of configuring a security inspection device are provided. In an embodiment, the security inspection system may include: an identity information entry device configured to enter an identification of an inspected person; a parameter determination device configured to determine a parameter for performing a security inspection on the inspected person based on a security factor of the inspected person determined according to user data corresponding to the identification of the inspected person; and a security inspection device configured to perform the security inspection on the inspected person based on the determined parameter. According to embodiments, it is possible to accurately predict the user's behavior and evaluate the risk or potential danger from the user by analyzing and mining the user's comprehensive data, and thus to provide a more accurate security inspection solution.

A terahertz security inspection robot is provided, including: a housing including a main housing and a head housing rotatably connected to the main housing; a terahertz wave imaging mechanism including a mirror assembly arranged in the head housing and a detector array arranged in the main housing; and a rotating mechanism configured to cause the head housing and the mirror assembly located in the head housing to rotate with respect to the main housing, so that the mirror assembly of the terahertz wave imaging mechanism is oriented in different directions to respectively perform terahertz scanning and imaging on objects to be inspected in different inspection regions in a security inspection scene.

In an inspection system that uses terahertz waves, in order to improve the inspection precision for objects and the like, the inspection system has an illumination unit having a plurality of illumination elements that radiate terahertz waves; a camera unit that captures images of the terahertz waves that have been reflected off of an object that has been irradiated by the plurality of illumination elements; and a control unit that performs control so as to make the light emission timings for the plurality of illumination elements different.

A computing-device implemented system and method for identifying an item in an x-ray image is described. The method includes training a machine learning algorithm with at least one training data set of x-ray images to generate at least one machine-learned model. The method further includes receiving at least one rendered x-ray image that includes an item, identifying the item using the at least one model, and generating an automated detection indication associated with the item.

The present disclosure relates to an Intelligent Surveillance Camera Control and Notification System. The system comprises: a plurality of camcorders; an infrared sensor coupled with each camera; a controlling unit; and an alert unit. The aim of the present disclosure is to provide a framework that can detect weapon for safety and security of public. The proposed invention manages the planning and execution of a clever observation checking framework utilizing a Raspberry Pi and a PIR sensor for cell phones. The proposed framework collects data and transmits it via a 3G Dongle to a PDA via a web application. Raspberry Pi operates and controls movement finders and camcorders for remote detection and reconnaissance, as well as transfers live video and records it for later playback. The proposed invention is advantageous because it provides dependability and security on both sides.

The present invention comprises a multi-modal security checkpoint. The security checkpoint can simultaneously scan for and simultaneously identify hidden metallics (e.g., weapons, shrapnel) and non-metallics (e.g., explosives, dielectrics). The security checkpoint performs scanning and identifying at a rate of 15 or more frames per second for all targets within the inspection area. The security checkpoint comprises blocks for sending and receiving radiation signals, the blocks comprising transmitters and/or receivers, the blocks being configured to share information to compare cross- and co-polarizations of signals emitted. The security checkpoint combines many threat detection technologies into one checkpoint that allows it to be robust and detect a large variety of threats in mass transit hubs requiring high throughput processing capabilities.

A system and method for classifying compartments at a security checkpoint includes classifying a compartment into a first category or a second category using a first stage neural network that analyzes a three-dimensional representation of the compartment extracted from an imaging device coupled to the computing system, and in response to classifying the compartment into the second category, screening the compartment using a second stage neural network that performs image segmentation to isolate a hazardous object present in the compartment.

Hidden camera detection systems and methods are disclosed herein. An example method includes illuminating a surface with infrared light, obtaining an image of the surface using an infrared camera, and determining the presence of a hidden camera associated with the surface by determining a difference in spectral reflectance between how the infrared light is reflected off of the hidden camera as compared to how the infrared light is reflected off of the surface.