Abstract: A method includes receiving a first time series of video frames depicting a first area of interest that includes a display area in a retail environment, and using classification by a convolutional neural network to detect instances of people picking up inventory items from the display area. The method also includes receiving a second time series of video frames depicting a second area of interest in the retail environment, and determining, based upon one or more portions of the second time series of video frames, that at least one inventory item picked up at the display area was not, or will not likely be, checked out. The method also includes causing one or more alert messages to be displayed, including causing an alert message to be displayed based on the determining.

Abstract: A method includes receiving a first time series of video frames depicting a first area of interest that includes a display area in a retail environment, and using classification by a convolutional neural network to detect instances of people picking up inventory items from the display area. The method also includes receiving a second time series of video frames depicting a second area of interest in the retail environment, and determining, based upon one or more portions of the second time series of video frames, that at least one inventory item picked up at the display area was not, or will not likely be, checked out. The method also includes causing one or more alert messages to be displayed, including causing an alert message to be displayed based on the determining.

Abstract: Methods and systems monitor activity in a retail environment, such as activity in a display area (e.g., aisle) of the retail environment. A convolutional neural network is used to detect objects (e.g., inventory items) or events (e.g., instances of people picking up inventory items from shelves). Various algorithms may be used to determine whether suspicious activity occurs, and/or to determine whether to trigger alerts. Monitored/detected activity may be stored in a database to facilitate a deeper understanding of operations within the retail environment.

Abstract: Methods and systems monitor activity in a retail environment, such as activity in a checkout area (e.g., checkout station) of the retail environment. A convolutional neural network is used to detect objects (e.g., inventory items) or events. Various algorithms may be used to determine whether valid checkout procedures are followed, and/or to determine whether to trigger alerts. Monitored/detected activity may be stored in a database to facilitate a deeper understanding of operations within the retail environment.