Abstract: A device may process the surveillance video data to segment vehicles, and may utilize a segmentation guided attention network model with the vehicles to determine traffic density count data. The device may process an image segmentation map, with a regression analysis model, to derive traffic signal timing. The device may process the surveillance video data, with a deep learning model, to identify objects, and may utilize a YOLO model, with the objects, to determine object types. The device may utilize a curriculum loss model with the objects to determine crowd count data, and may process the surveillance video data, with a video analytics model, to identify first events. The device may process the surveillance video data, with a classifier and deep network models, to identify second events, and may process the determined information, with a dynamic text-based explanation model, to generate a text-based explanation and/or a failure prediction.

Abstract: A device may process the surveillance video data to segment vehicles, and may utilize a segmentation guided attention network model with the vehicles to determine traffic density count data. The device may process an image segmentation map, with a regression analysis model, to derive traffic signal timing. The device may process the surveillance video data, with a deep learning model, to identify objects, and may utilize a YOLO model, with the objects, to determine object types. The device may utilize a curriculum loss model with the objects to determine crowd count data, and may process the surveillance video data, with a video analytics model, to identify first events. The device may process the surveillance video data, with a classifier and deep network models, to identify second events, and may process the determined information, with a dynamic text-based explanation model, to generate a text-based explanation and/or a failure prediction.