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: 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.