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Frames labeled in cinema (FLIC)
2D Classification
许可协议: Unknown

Overview

From the paper: We collected a 5003 image dataset automatically from popular Hollywood movies. The images were obtained by running a state-of-the-art person detector on every tenth frame of 30 movies. People detected with high confidence (roughly 20K candidates) were then sent to the crowdsourcing marketplace Amazon Mechanical Turk to obtain groundtruthlabeling. Each image was annotated by five Turkers for $0.01 each to label 10 upperbody joints. The median-of-five labeling was taken in each image to be robust to outlier annotation. Finally, images were rejected manually by us if the person was occluded or severely non-frontal. We set aside 20% (1016 images) of the data for testing.

数据概要
数据格式
image,
数据量
--
文件大小
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发布方
Sapp,Benjamin,Ben Taskar
| 数据量 -- | 大小 --
Frames labeled in cinema (FLIC)
2D Classification
许可协议: Unknown

Overview

From the paper: We collected a 5003 image dataset automatically from popular Hollywood movies. The images were obtained by running a state-of-the-art person detector on every tenth frame of 30 movies. People detected with high confidence (roughly 20K candidates) were then sent to the crowdsourcing marketplace Amazon Mechanical Turk to obtain groundtruthlabeling. Each image was annotated by five Turkers for $0.01 each to label 10 upperbody joints. The median-of-five labeling was taken in each image to be robust to outlier annotation. Finally, images were rejected manually by us if the person was occluded or severely non-frontal. We set aside 20% (1016 images) of the data for testing.

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