Pedestrian dataset from MIT： 行人圖片背景簡單

INRIA 數(shù)據(jù)集?：行人檢測圖片，背景復(fù)雜

WallFlower dataset: 用于評(píng)價(jià)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(shí)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(jù)集.
Caltech image collections: 用于目標(biāo)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(gè)ground-truthed dataset

MIT Face Dataset
MIT Car Datasets
MIT Street Scenes: CBCL StreetScenes Challenge Framework 是一個(gè)圖像、注釋、軟件和性能檢測的對(duì)象集[cars, pedestrians, bicycles, buildings, trees, skies, roads, sidewalks, and stores]
LabelMe Dataset: 超過150,000已經(jīng)標(biāo)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(jù)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(jù)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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