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计算机视觉数据集

發(fā)布時間:2025/3/21 编程问答 14 豆豆
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Pedestrian dataset from MIT: 行人圖片背景簡單

INRIA 數(shù)據集?:行人檢測圖片,背景復雜

WallFlower dataset: 用于評價背景建模算法的好壞. Ground-truth foreground provided.
Foreground/Background segmentation and Stereo dataset: from Microsoft Cambridge.
VISOR: Video Surveillance Online Repositiory: 大量的視頻和路面實況.
3D Photography Dataset
Multi-model, multi-camera meeting room dataset
Advanced Video and Signal based Surveillance: 各種用于跟蹤和檢測的數(shù)據集.
Caltech image collections: 用于目標物體檢測,分割和分類
INRIA Datasets: 車輛, 人, 馬, 人類行為等


CAVIAR surveillance Dataset
Videos for Head Tracking
Pedestrian dataset from MIT
Shadow detection datasets
Flash and non-Flash dataset
Experiments on skin region detection and tracking: 包括一個ground-truthed dataset


MIT Face Dataset
MIT Car Datasets
MIT Street Scenes: CBCL StreetScenes Challenge Framework 是一個圖像、注釋、軟件和性能檢測的對象集[cars, pedestrians, bicycles, buildings, trees, skies, roads, sidewalks, and stores]
LabelMe Dataset: 超過150,000已經標注的照片.
MuHAVi: Multicamera Human Action Video DataA large body of human action video data using 8 cameras. Includes manually annotated silhouette data. 用于測試人行為的數(shù)據集
INRIA Xmas Motion Acquisition Sequences (IXMAS): Multiview dataset for view-invariant human action recognition.
i-LIDS datasets: UK Government benchmark datasets for automated surveillance.
The Daimler Pedestrian Detection Benchmark: contains 15,560 pedestrian and non-pedestrian samples (image cut-outs) and 6744 additional full images not containing pedestrians for bootstrapping. The test set contains more than 21,790 images with 56,492 pedestrian labels (fully visible or partially occluded), captured from a vehicle in urban traffic.
Stereo Pedestrian Detection Evaluation Dataset: a dataset for evaluating pedestrian detection using stereo camera images and

video. 用于測試行人檢測算法的數(shù)據集
Colour video and Thermal infrared datasets: Dataset of videos in colour and thermal infrared. Videos are aligned temporally and spatially. Ground-truth for object tracking is provided.


from:?http://blog.csdn.net/lcj_cjfykx/article/details/21217551

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