Abstract: Techniques are generally described for user-customized computer vision event detection. A camera device may capture a first frame of image data. A first machine learning model may generate first embedding data for the first frame of image data. The first embedding data may be input into a second machine learning model associated with the camera device. The second machine learning model and the first embedding data may be used to determine that an area-of-interest represented by the first frame of image data is in a first state. State data stored in memory may be determined. The state data may indicate that the area-of-interest was previously in a second state. An alert may be sent to a device associated with the camera device indicating that the area-of-interest has changed from the second state to the first state.

Abstract: Techniques are generally described for user-customized computer vision event detection. A camera device may capture a first frame of image data. A first machine learning model may generate first embedding data for the first frame of image data. The first embedding data may be input into a second machine learning model associated with the camera device. The second machine learning model and the first embedding data may be used to determine that an area-of-interest represented by the first frame of image data is in a first state. State data stored in memory may be determined. The state data may indicate that the area-of-interest was previously in a second state. An alert may be sent to a device associated with the camera device indicating that the area-of-interest has changed from the second state to the first state.