Moving Object Detection

The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that are being modelled in practice, e.g. satellite, microscopic and gaming, making it difficult to assert the degree of generalization learned by the model. We introduce the Roboflow-100 (RF100) consisting of 100 datasets, 7 imagery domains, 224,714 images, and 805 class labels with over 11,170 labelling hours. We derived RF100 from over 90,000 public datasets, 60 million public images that are actively being assembled and labelled by computer vision practitioners in the open on the web application Roboflow Universe. By releasing RF100, we aim to provide a semantically diverse, multi-domain benchmark of datasets to help researchers test their model's generalizability with real-life data. RF100 download and benchmark replication are available on GitHub.

This dataset is designed to enhance the progress of event-based optical flow algorithms. The data was collected using the IniVation DAViS346 camera, which has a 346 x 260 spatial resolution. The dataset is classified into camera motion data (stationary scene and moving camera) and object motion data (stationary camera and moving objects). The camera motion data contains four real indoor sequences (namely, checkerboard, classroom, conference room, and conference room translation) with ground truth motion inferred from IMU. The movement of the camera in this category was restricted by a gimbal, and the IMU was calibrated before each collection. The object motion data includes two real sequences (called hands and cars) containing multiple object motions. This category does not have ground-truth motion since the object motion cannot be inferred from IMU.