Abstract: Example implementations include a method, apparatus, and computer-readable medium for object detection, comprising detecting a first object in a first image frame and a second image frame, wherein the first object is bounded by region-of-interest (ROI) boundaries generated by an ROI detection model. The implementations further include calculating a speed of the first object using positions of the first object in the first and second image frame, identifying at least one image frame that should include the first object based on a calculated speed of the first object. The implementations further include determining that the at least one image frame should be added to a training dataset for the ROI detection model in response to detecting that the ROI detection model did not generate a ROI boundary in the at least one image frame, and subsequently re-training the ROI detection model using said training dataset.

Abstract: Disclosed herein is an object detection system, including apparatuses and methods for object detection. An implementation may include receiving a first image frame from an ROI detection model that generated a first ROI boundary around a first object detected in the first image frame and subsequently receiving a second image frame. The implementation further includes predicting, using an ROI tracking model, that the first ROI boundary will be present in the second image frame and then detecting whether the first ROI boundary is in fact present in the second image frame. The implementation includes determining that the second image frame should be added to a training dataset for the ROI detection model when detecting that the ROI detection model did not generate the first ROI boundary in the second image frame as predicted and re-training the ROI detection model using the training dataset.

Abstract: Disclosed herein is an object detection system, including apparatuses and methods for object detection. An implementation may include receiving a first class of a first object depicted in an image frame from a classification model and subsequently receiving a second image frame. The implementation further includes predicting, using a classification tracking model, that the classification model will output the first class for the second image frame and then detecting whether the first class is in fact outputted. The implementation includes determining that the second image frame should be added to a training dataset for the classification model when detecting that the classification model did not generate the first class for the second image frame as predicted and re-training the classification model using the training dataset.

Abstract: Disclosed herein is an object detection system, including apparatuses and methods for object detection. An implementation may include receiving a first image frame from an ROI detection model that generated a first ROI boundary around a first object detected in the first image frame and subsequently receiving a second image frame. The implementation further includes predicting, using an ROI tracking model, that the first ROI boundary will be present in the second image frame and then detecting whether the first ROI boundary is in fact present in the second image frame. The implementation includes determining that the second image frame should be added to a training dataset for the ROI detection model when detecting that the ROI detection model did not generate the first ROI boundary in the second image frame as predicted and re-training the ROI detection model using the training dataset.

Abstract: Disclosed herein is an object detection system, including apparatuses and methods for object detection. An implementation may include receiving a first image frame from an ROI detection model that generated a first ROI boundary around a first object detected in the first image frame and subsequently receiving a second image frame. The implementation further includes predicting, using an ROI tracking model, that the first ROI boundary will be present in the second image frame and then detecting whether the first ROI boundary is in fact present in the second image frame. The implementation includes determining that the second image frame should be added to a training dataset for the ROI detection model when detecting that the ROI detection model did not generate the first ROI boundary in the second image frame as predicted and re-training the ROI detection model using the training dataset.

Abstract: Disclosed herein is an object detection system, including apparatuses and methods for object detection. An implementation may include receiving a first image frame from an ROI detection model that generated a first ROI boundary around a first object detected in the first image frame and subsequently receiving a second image frame. The implementation further includes predicting, using an ROI tracking model, that the first ROI boundary will be present in the second image frame and then detecting whether the first ROI boundary is in fact present in the second image frame. The implementation includes determining that the second image frame should be added to a training dataset for the ROI detection model when detecting that the ROI detection model did not generate the first ROI boundary in the second image frame as predicted and re-training the ROI detection model using the training dataset.

Abstract: Techniques are disclosed for automating GUI development from a GUI screen image that includes text information and one or more graphic user interface components. The GUI screen image is analyzed to extract text information and to identify the UI components included in the GUI screen. One or more text regions in the GUI screen image are detected and are replaced with placeholders. Images of one or more graphic user interface components in the GUI screen are extracted from the GUI screen image and are classified using a machine learning-based classifier. A GUI model is generated for the GUI based upon the classification results, locations of the one or more text regions, and locations of the one or more graphic user interface components. The generated model can then be used to generate one or more implementations (e.g., executable code) of the GUI, possibly for various platforms in different programming languages.

Abstract: Techniques are disclosed for automating GUI development from a GUI screen image that includes text information and one or more graphic user interface components. The GUI screen image is analyzed to extract text information and to identify the UI components included in the GUI screen. One or more text regions in the GUI screen image are detected and are replaced with placeholders. Images of one or more graphic user interface components in the GUI screen are extracted from the GUI screen image and are classified using a machine learning-based classifier. A GUI model is generated for the GUI based upon the classification results, locations of the one or more text regions, and locations of the one or more graphic user interface components. The generated model can then be used to generate one or more implementations (e.g., executable code) of the GUI, possibly for various platforms in different programming languages.