Abstract: A new approach is proposed that supports protocol compliance by a person in various workplace applications and environments. The proposed approach determines if a person is following a set of protocols/procedures created and defined to ensure safety and efficiency of the workers/employees in his/her workplace environment. This proposed approach focuses on specifying one or more zones of interest, identifying presence of the person and/or an object associated with the person in the one or more zones of interest, classifying a sequence of activities and/or postures of the person, and determining the durations of the activities. A is notified if it is determined that the person is not in compliance with the set of protocols in the workplace environment. In addition, data collected from the one or more zones of interest is stored securely in a local site to protect confidentiality of production processes as well as privacy of the person.

Abstract: A new approach is proposed to support activity tracking of a person for protocol compliance. The proposed approach tracks a sequence of activities of the person at one or more zones of interest being monitored via one or more cameras and/or sensors to determine if the person is following a set of pre-determined protocols at the zones of interest. Under the proposed approach, a plurality of AI models are trained and utilized to define the one or more zones of interest, to detect presence and classification of the person and/or an object associated with the person, to determine/classify the sequence of activities of the person, and to determine duration of the sequence of activities. The sequence of activities of the person is then checked against the set of pre-determined protocols to determine if the person is in protocol compliance or not and protocol violations are reported to a user.

Abstract: A method includes receiving a data stream from an input device at a monitored location. The data stream is processed to determine whether an abnormal event has occurred. The method further includes transmitting data associated with whether the abnormal event has occurred to a user. Data associated with user actions in response to the transmitting data is collected. The method finally includes generating a machine learning model based on the received data stream, the processed data stream and whether the abnormal event has occurred, and further the collected data associated with user actions in response to the transmitting.

Abstract: A new approach is proposed that contemplates systems and methods to monitor the premises, e.g., home, office facility, manufacturing floor, healthcare facility, nursing home, etc., to detect an abnormal activity, e.g., fire, smoke, flood, intrusion, fall, stroke, etc., in a smart fashion by leveraging machine learning (ML) model. The method includes receiving a data stream from an input device at a monitored location. The data stream is processed to determine a pose and a position of a person at the monitored location. It is determined whether an abnormal activity has occurred based on the pose and the position of the person. A message is transmitted to a user in response to the determining.

Abstract: A new approach is proposed to support efficient machine learning (ML) model training for a monitoring system using only a few images from a video image stream collected by a camera. First, a set of 2-dimensional (2D) images of a person is produced from the collected video image stream at various poses and/or positions to identify the person's ordinary/normal activities at the monitored location. The set of 2D images is then transferred under a plurality of contexts representing different orientations and/or heights of the camera with derived embedding codes to train one or more ML models. Once trained, the one or more ML models are applied to filter the video stream at the monitored location and to alert an administrator if an abnormal activity is detected from the video streams captured at the monitored location based on the trained one or more ML models of the person's normal activity.

Abstract: A new approach is proposed to support efficient user privacy protection for security monitoring. A set of stick figures depicting a human body of a user is extracted from a set of still images taken over a period of time in a collected video stream at a monitored location. An activity of the user at the monitored location is then recognized based on analysis of the one or more stick figures in each of the one or more still images taken from the video stream over the period of time. In some embodiments, at least a portion of the human body of the user is pixelized to ensure protection of the user's privacy data while still enabling the security monitoring system to effectively perform its security monitoring functions. Additionally, the captured privacy data of the user is securely stored at a local site to further ensure privacy of the user.

Abstract: A monitoring system includes sensors that monitor activity within a designated territory. The sensors include visual sensors that make video recordings. A local processing system located within or proximate to the designated territory receives signals from the sensors. The local processing system processes and analyzes the signals from the sensors to produce messages that describe activity within the designated territory as monitored by the sensors. The messages do not include audio, visual or other direct identifying information that directly reveal identity of persons within the designated territory. A monitoring station outside the designated territory receives the messages produced by the local processing system and makes the messages available to external observers.