Motion Detection is a process to detect the presence of any moving entity in an area of interest. Motion Detection is of great importance due to its application in various areas such as surveillance and security, smart homes, and health monitoring.
The nuScenes dataset is a large-scale autonomous driving dataset. The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore. Each scene is 20 seconds long and annotated at 2Hz. This results in a total of 28130 samples for training, 6019 samples for validation and 6008 samples for testing. The dataset has the full autonomous vehicle data suite: 32-beam LiDAR, 6 cameras and radars with complete 360° coverage. The 3D object detection challenge evaluates the performance on 10 classes: cars, trucks, buses, trailers, construction vehicles, pedestrians, motorcycles, bicycles, traffic cones and barriers. Source: PointPainting: Sequential Fusion for 3D Object Detection
Extension of the official KITTI'15 dataset. independently moving instance segmentation ground truth to cover all moving objects, not just a selection of cars and vans. - Instance Motion Segmentation of all moving objects - Binary Motion Segmentation (background/foreground) - Validation Masks Dataset contains: - Instance Motion Segmenation for the training split of the KITTI'15 dataset