Automatic Ground Truths: Projected Image Annotations for Omnidirectional Vision
We present a novel data set made up of omnidirectional video of multiple objects whose centroid positions are annotated automatically. Omnidirectional vision is an active field of research focused on the use of spherical imagery in video analysis and scene understanding, involving tasks such as object detection, tracking and recognition. Our goal is to provide a large and consistently annotated video data set that can be used to train and evaluate new algorithms for these tasks. Here we describe the experimental setup and software environment used to capture and map the 3D ground truth positions of multiple objects into the image. Furthermore, we estimate the expected systematic error on the mapped positions. In addition to final data products, we release publicly the software tools and raw data necessary to re-calibrate the camera and/or redo this mapping. The software also provides a simple framework for comparing the results of standard image annotation tools or visual tracking systems against our mapped ground truth annotations.
Code (0)
등록된 구현이 없습니다.
Tasks
object-detectionObject DetectionScene UnderstandingVisual TrackingSimilar Papers 제목 키워드 기반
Learning Object Scale With Click Supervision for Object Detection
Weakly-supervised object detection has recently attracted increasing attention since it only requires image-levelannotations. However, the performance obtained by existingmethods is still far from being satisfactory comp…
Objectobject-detectionObject DetectionWeakly Supervised Object DetectionDiscrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images
Weakly supervised methods, such as class activation maps (CAM) based, have been applied to achieve bleeding segmentation with low annotation efforts in Wireless Capsule Endoscopy (WCE) images. However, the CAM labels ten…
Active LearningDecoderPseudo LabelSemi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time
Estimating 3D hand and object pose from a single image is an extremely challenging problem: hands and objects are often self-occluded during interactions, and the 3D annotations are scarce as even humans cannot directly …
3D Hand Pose Estimationhand-object poseHand Pose EstimationObject+1LayoutMP3D: Layout Annotation of Matterport3D
Inferring the information of 3D layout from a single equirectangular panorama is crucial for numerous applications of virtual reality or robotics (e.g., scene understanding and navigation). To achieve this, several datas…
Scene UnderstandingW2N:Switching From Weak Supervision to Noisy Supervision for Object Detection
Weakly-supervised object detection (WSOD) aims to train an object detector only requiring the image-level annotations. Recently, some works have managed to select the accurate boxes generated from a well-trained WSOD net…
Objectobject-detectionObject DetectionTransfer Learning+1