Abstract: Artificial intelligence and computer vision analysis is used to generate ergonomics-annotated human forms and location histories in a video sequence. Video images are analyzed to identify human forms and generate a wireframe representing each human form, which is then analyzed to identify ergonomic risks which can be ranked according to severity. Severity markings for the ergonomic risks are superimposed on a location history of the human form to show the history, and the severity markings may also be superimposed on the human form, for example on a superimposed representation of the wireframe, by body region (e.g. neck, lower back). Optionally, where the human form represents a worker or other individual having planned activities, the ergonomic risks can be compared to a task flow for the worker to determine whether the ergonomic risks occurred during the planned activities or during unplanned activities.

Abstract: The present disclosure uses automated, computer-driven analysis of video feeds to extract raw motion metadata, identify moving objects and assess whether or not the moving objects are compliant with certain predefined rules. The motion can be visually plotted on a background in the form of motion trails, and a chronological plot of the motion may be provided as a menu to find anomalies through visual screening. The motion metadata may be filtered through annotations. A highlighted portion of the video feed relating to a specific topic of interest may be replayed.

Abstract: The present disclosure uses automated, computer-driven analysis of video feeds to extract raw motion metadata, identify moving objects and assess whether or not the moving objects are compliant with certain predefined rules. The motion can be visually plotted on a background in the form of motion trails, and a chronological plot of the motion may be provided as a menu to find anomalies through visual screening. The motion metadata may be filtered through annotations. A highlighted portion of the video feed relating to a specific topic of interest may be replayed.

Abstract: Artificial intelligence and computer vision analysis is used to generate ergonomics-annotated human forms and location histories in a video sequence. Video images are analyzed to identify human forms and generate a wireframe representing each human form, which is then analyzed to identify ergonomic risks which can be ranked according to severity. Severity markings for the ergonomic risks are superimposed on a location history of the human form to show the history, and the severity markings may also be superimposed on the human form, for example on a superimposed representation of the wireframe, by body region (e.g. neck, lower back).

Abstract: A computer-implemented method of anonymizing at least one feature in a scene comprises receiving one or more images representing the scene and applying a sorting algorithm to the image(s) to identify features within the image(s) and classify the features according to at least one predetermined feature category to produce at least one classified feature in the image(s). An annotation scheme, specifying at least one obscuring annotation type to be applied to at least one corresponding feature category, is applied to the image(s). The image(s) are then modified by, for each classified feature in the image(s) having an annotation indicating that said classified feature is in a feature category corresponding to a particular obscuring annotation type to be applied, applying the particular obscuring annotation type to that classified feature in the image(s) so that at least one identifying aspect of that classified feature is obscured within the image(s).