Abstract: A transportation security system, having a set of screening node devices located at one or more airports, each having image capturing equipment deployed thereat by which image data are generated, the image data comprising individual images of objects passing through the image capturing equipment; a set of image interpretation node devices at which it is determined whether a security threat is depicted in the image data generated at the screening node devices; and a multiplexing control node having a processor coupled to memory and configured to assign an image, from the image data generated by the set of screening nodes, to an image interpretation node of the set of image interpretation nodes for analysis, a method, and a transportation security apparatus related thereto.

Abstract: A transportation security system, having a set of screening node devices located at one or more airports, each having image capturing equipment deployed thereat by which image data are generated, the image data comprising individual images of objects passing through the image capturing equipment; a set of image interpretation node devices at which it is determined whether a security threat is depicted in the image data generated at the screening node devices; and a multiplexing control node having a processor coupled to memory and configured to assign an image, from the image data generated by the set of screening nodes, to an image interpretation node of the set of image interpretation nodes for analysis, a method, and a transportation security apparatus related thereto.

Abstract: In a transportation security technique, images are stored that are received from image capturing equipment deployed at respective screening nodes. The images are analyzed using a machine learning model, where presence of a particular object in an image indicates that a threat condition exists at the screening node. The analyzed images are transmitted to threat assessment components in accordance with predetermined criteria. An indication that the particular object is observed in the image is received from the threat assessment components. An indication that the particular object is observed in the image is transmitted to the screening node responsive to receiving the indication that the particular object is observed in the image. An indication of whether the particular object is present at the screening node is received. The machine learning model is trained based on the received indication of whether the particular object is observed in the image.

Abstract: In a transportation security technique, images are stored that are received from image capturing equipment deployed at respective screening nodes. The images are analyzed using a machine learning model, where presence of a particular object in an image indicates that a threat condition exists at the screening node. The analyzed images are transmitted to threat assessment components in accordance with predetermined criteria. An indication that the particular object is observed in the image is received from the threat assessment components. An indication that the particular object is observed in the image is transmitted to the screening node responsive to receiving the indication that the particular object is observed in the image. An indication of whether the particular object is present at the screening node is received. The machine learning model is trained based on the received indication of whether the particular object is observed in the image.

Abstract: In a transportation security technique, images are stored that are received from image capturing equipment deployed at respective screening nodes. The images are analyzed using a machine learning model, where presence of a particular object in an image indicates that a threat condition exists at the screening node. The analyzed images are transmitted to threat assessment components in accordance with predetermined criteria. An indication that the particular object is observed in the image is received from the threat assessment components. An indication that the particular object is observed in the image is transmitted to the screening node responsive to receiving the indication that the particular object is observed in the image. An indication of whether the particular object is present at the screening node is received. The machine learning model is trained based on the received indication of whether the particular object is observed in the image.

Abstract: In a transportation security technique, images are stored that are received from image capturing equipment deployed at respective screening nodes. The images are analyzed using a machine learning model, where presence of a particular object in an image indicates that a threat condition exists at the screening node. The analyzed images are transmitted to threat assessment components in accordance with predetermined criteria. An indication that the particular object is observed in the image is received from the threat assessment components. An indication that the particular object is observed in the image is transmitted to the screening node responsive to receiving the indication that the particular object is observed in the image. An indication of whether the particular object is present at the screening node is received. The machine learning model is trained based on the received indication of whether the particular object is observed in the image.

Abstract: In a transportation security technique, images are stored that are received from image capturing equipment deployed at respective screening nodes. The images are analyzed using a machine learning model, where presence of a particular object in an image indicates that a threat condition exists at the screening node. The analyzed images are transmitted to threat assessment components in accordance with predetermined criteria. An indication that the particular object is observed in the image is received from the threat assessment components. An indication that the particular object is observed in the image is transmitted to the screening node responsive to receiving the indication that the particular object is observed in the image. An indication of whether the particular object is present at the screening node is received. The machine learning model is trained based on the received indication of whether the particular object is observed in the image.

Abstract: In a transportation security technique, images are stored that are received from image capturing equipment deployed at respective screening nodes. The images are analyzed using a machine learning model, where presence of a particular object in an image indicates that a threat condition exists at the screening node. The analyzed images are transmitted to threat assessment components in accordance with predetermined criteria. An indication that the particular object is observed in the image is received from the threat assessment components. An indication that the particular object is observed in the image is transmitted to the screening node responsive to receiving the indication that the particular object is observed in the image. An indication of whether the particular object is present at the screening node is received. The machine learning model is trained based on the received indication of whether the particular object is observed in the image.