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Please cite: | ||||||
(1) |
Segmentation and Recognition
Using Structure from Motion Point Clouds, ECCV 2008 (pdf) Brostow, Shotton, Fauqueur, Cipolla (bibtex) |
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(2) |
Semantic Object Classes in
Video: A High-Definition Ground Truth Database (pdf) Pattern Recognition Letters (to appear) Brostow, Fauqueur, Cipolla (bibtex) |
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Description: |
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The Cambridge-driving Labeled
Video Database (CamVid) is the first collection of videos with object
class semantic labels, complete with metadata. The database provides
ground truth labels that associate each pixel with one of 32 semantic classes. The database addresses the need for experimental data to quantitatively evaluate emerging algorithms. While most videos are filmed with fixed-position CCTV-style cameras, our data was captured from the perspective of a driving automobile. The driving scenario increases the number and heterogeneity of the observed object classes. Over ten minutes of high quality 30Hz footage is being provided, with corresponding semantically labeled images at 1Hz and in part, 15Hz. The CamVid Database offers four contributions that are relevant to object analysis researchers. First, the per-pixel semantic segmentation of over 700 images was specified manually, and was then inspected and confirmed by a second person for accuracy. Second, the high-quality and large resolution color video images in the database represent valuable extended duration digitized footage to those interested in driving scenarios or ego-motion. Third, we filmed calibration sequences for the camera color response and intrinsics, and computed a 3D camera pose for each frame in the sequences. Finally, in support of expanding this or other databases, we offer custom-made labeling software for assisting users who wish to paint precise class-labels for other images and videos. We evaluated the relevance of the database by measuring the performance of an algorithm from each of three distinct domains: multi-class object recognition, pedestrian detection, and label propagation. |
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Overview Video: |
Avi, 30 Mb, xVid compressed. (playback tips or get the free Mac/Windows player. or Mpg, 11 Mb, mpeg-1 compressed (more compatible, but lower quality) |
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CamVid Database (just samples shown. For all the videos, see below) |
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Original Video Sequences: |
Link to FTP server with
video files (very big!) Link to codecs + utility for extracting frames from those big files (read the inventory.txt) |
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Labeled Images (701 so far) |
Link to zip file with painted class labels for stills from the video sequences. Txt file listing classes and label colors as RGB triples (sorted). (Note: the corresponding raw input images only - at 1Hz, already extracted from the respective videos are here temporarily(556Mb).) |
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Camera extrinsics |
Link
to files and code (if link breaks someday, go here) The relevant line that you care about to get the projection matrix of 1 camera is in MotBoostEvalOneFrame.m (see how LoadBoujou_2Dtrax_3dBans_Misc.m calls it): curC = Cs( frameNum-offsetForFrameNums, 1:3); |
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Example
camera pose
trajectory, stored in Boujou Animation Format: each line containing "AddDecompCameraKey" has a K and R matrix and t vector, so that P = K * R * [I -t] |
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seq06R0 |
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seq16E5
Description: 6120 frames at 30Hz == 3:24 min Sample Frame VideoFiles 1 and 2 in MXF format* (note: these are 2 halves of 1 zip file) seq16E5_15Hz (see also CamSeq01) Description: 202 frames at 30Hz == 0:06 min Sample Frame VideoFiles 1 and 2 in MXF format* (note: same files as above, but use a different script) |
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seq05VD
Description: 5130 frames at 30Hz == 2:51 min Sample Frame VideoFile in MXF format* |
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seq01TP |
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Listing of (RGB)-Class assignments (alphabetical) Listing in color-order used by MSRC (with "XX") | |||||||
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