{"id":58340,"date":"2020-08-05T14:58:38","date_gmt":"2020-08-05T05:58:38","guid":{"rendered":"https:\/\/smilegate.ai\/?p=58340"},"modified":"2020-08-14T11:23:06","modified_gmt":"2020-08-14T02:23:06","slug":"mit-driveseg","status":"publish","type":"post","link":"https:\/\/smilegate.ai\/en\/2020\/08\/05\/mit-driveseg\/","title":{"rendered":"MIT DriveSeg-data for road situation awareness research"},"content":{"rendered":"

It is a dataset DriveSeg created for research on road situation awareness (used for self-driving cars, etc.). For each frame of the video, the entire image is pixel-by-pixel semantic labeling. Labels are 12 categories of \u201cvehicle, pedestrian, road, sidewalk, bicycle, motorcycle, building, terrain (horizontal vegetation), vegetation (vertical vegetation), pole, traffic light, and traffic sign\u201d.<\/p>\n\n\n\n

The human-handed version of this process is provided at 5,000 frames, 1080p@30Hz, and the semi-auto version at 20,000 frames, 720p@30Hz. Even if it is not an autonomous vehicle, it will be useful in segmentation vision research. (I plan to use it for separating a specific object from an image, replacing the background, and improving the performance of the segmentation algorithm)<\/p>\n\n\n\n

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MIT DriveSeg Dataset for Dynamic Driving Scene Segmentation | MIT AgeLab<\/div>
To date, self-driving data made available to the research community have primarily consisted of troves of static, single images that can be used to identify and track common objects found in and around the road, such as bicycles, pedestrians or traffic lights through the use of \u201cbounding boxes.\u201d<\/div><\/div><\/div>
<\/span><\/div>","protected":false},"excerpt":{"rendered":"

(\uc790\uc728\uc8fc\ud589\ucc28 \ub4f1\uc5d0 \uc0ac\uc6a9\ub420) \ub3c4\ub85c \uc0c1\ud669 \uc778\uc2dd \uc5f0\uad6c\ub97c \uc704\ud574 \ub9cc\ub4e4\uc5b4\uc9c4 \ub370\uc774\ud130\uc14b DriveSeg\uc785\ub2c8\ub2e4. \ub3d9\uc601\uc0c1\uc758 \uac01 \ud504\ub808\uc784\uc5d0 \ub300\ud574, \uc774\ubbf8\uc9c0 \uc804\uccb4\ub97c pixel-by-pixel\ub85c semantic labeling \ud55c \uac83\uc785\ub2c8\ub2e4. Label\uc740 “vehicle, pedestrian, road, sidewalk, bicycle, motorcycle, building, terrain (horizontal vegetation), vegetation (vertical vegetation), pole, traffic light, and traffic sign”\uc758 12\uac00\uc9c0\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc744 \uc778\uac04\uc774 \uc190\uc73c\ub85c \uc77c\uc77c\uc774 \ub2e4 \ud55c \ubc84\uc804\uc740 5\ucc9c\ud504\ub808\uc784, 1080p@30Hz, \uc138\ubbf8\uc624\ud1a0\ub85c \ud55c…<\/p>\n

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