Abstract: An operator behavior monitoring system, which includes: an operator behavior recognition system comprising hardware including a processor, a data storage facility and input/output interfaces, the system being configured to implement a set of convolutional neural networks including: an object detection group for detecting an object in the image and to delineate the object from the image; a feature extraction group which extracts features of the object detected by the object detection group; a classifier group which assess the features and classifying the features into a events and which is operable to report the events to a remote server; a server operable to communicate with the operator behavior recognition system for receiving predefined events detected by the operator behavior recognition system; a database in communication with the server, operable to store and retrieve detected operator incidents; and a web frontend, in communication with the server for interfacing with the server.

Abstract: A machine-implemented method for automated detection of drowsiness, which includes receiving from an imaging device directed at the face of an operator a series of images of the face of the operator onto processing hardware, on the processor detecting facial landmarks of the operator from the series of images to determine the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator, on the processor detecting the facial pose of the operator from the series of images to determine the level of gaze fixation by the operator, on the processor calculating the level of drowsiness of the operator by ensembling the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator and the level of gaze fixation by the operator, and generating an alarm when the calculated level of drowsiness of the operator exceeds a predefined value.

Abstract: An operator behavior monitoring system, which includes: an operator behavior recognition system comprising hardware including a processor, a data storage facility and input/output interfaces, the system being configured to implement a set of convolutional neural networks including: an object detection group for detecting an object in the image and to delineate the object from the image; a feature extraction group which extracts features of the object detected by the object detection group; a classifier group which assess the features and classifying the features into a events and which is operable to report the events to a remote server; a server operable to communicate with the operator behavior recognition system for receiving predefined events detected by the operator behavior recognition system; a database in communication with the server, operable to store and retrieve detected operator incidents; and a web frontend, in communication with the server for interfacing with the server

Abstract: An operator behavior recognition system comprising hardware including at least one processor, a data storage facility in communication with the processor and input/output interfaces in communication with the processor, the hardware being configured to implement a set of convolutional neural networks (CNNs) including: an object detection group into which at least one image is received from an image source for detecting at least one object in the image and to delineate the object from the image for further processing, at least one of the objects being detected being a face of a person; a facial features extraction group into which the image of the person's face is received and from which facial features from the person's face are extracted; and a classifier group which assess the facial features received from the facial feature extraction group in combination with objects detected by the object detection group to classify predefined operator behaviors.

Abstract: A machine-implemented method for automated detection of drowsiness, which includes receiving from an imaging device directed at the face of an operator a series of images of the face of the operator onto processing hardware, on the processor detecting facial landmarks of the operator from the series of images to determine the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator, on the processor detecting the facial pose of the operator from the series of images to determine the level of gaze fixation by the operator, on the processor calculating the level of drowsiness of the operator by ensembling the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator and the level of gaze fixation by the operator, and generating an alarm when the calculated level of drowsiness of the operator exceeds a predefined value.

Abstract: A system and method for calibrating a camera includes an energy source and a camera to be calibrated, with at least one of the energy source and the camera being mounted on a mechanical actuator so that it is movable relative to the other. A processor is connected to the energy source, the mechanical actuator and the camera and is programmed to control the mechanical actuator to move at least one of the energy source and the camera relative to the other through a plurality of discrete points on a calibration target pattern. The processor further, at each of the discrete points, controls the camera to take a digital image and perform a lens distortion characterization on each image. A focal length of the camera is determined including any lens connected to the camera and an extrinsic camera position for each image is then determined.

Abstract: A system and method for calibrating a camera includes an energy source and a camera to be calibrated, with at least one of the energy source and the camera being mounted on a mechanical actuator so that it is movable relative to the other. A processor is connected to the energy source, the mechanical actuator and the camera and is programmed to control the mechanical actuator to move at least one of the energy source and the camera relative to the other through a plurality of discrete points on a calibration target pattern. The processor further, at each of the discrete points, controls the camera to take a digital image and perform a lens distortion characterisation on each image. A focal length of the camera is determined including any lens connected to the camera and an extrinsic camera position for each image is then determined.