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Fine-Grained Classification of Pedestrians in Video: Benchmark and State of the Art

Hall, David C. and Perona, Pietro (2015) Fine-Grained Classification of Pedestrians in Video: Benchmark and State of the Art. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE , Piscataway, NJ, pp. 5482-5491. ISBN 978-1-4673-6964-0.

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A video dataset that is designed to study fine-grained categorisation of pedestrians is introduced. Pedestrians were recorded “in-the-wild” from a moving vehicle. Annotations include bounding boxes, tracks, 14 keypoints with occlusion information and the fine-grained categories of age (5 classes), sex (2 classes), weight (3 classes) and clothing style (4 classes). There are a total of 27,454 bounding box and pose labels across 4222 tracks. This dataset is designed to train and test algorithms for fine-grained categorisation of people; it is also useful for benchmarking tracking, detection and pose estimation of pedestrians. State-of-the-art algorithms for fine-grained classification and pose estimation were tested using the dataset and the results are reported as a useful performance baseline.

Item Type:Book Section
Related URLs:
URLURL TypeDescription Website Paper
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2015 IEEE. This work is funded by the ARO-JPL NASA Stennis NAS7.03001 grant and the Gordon and Betty Moore Foundation.
Funding AgencyGrant Number
Army Research Office (ARO)UNSPECIFIED
Gordon and Betty Moore FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20150601-132456354
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:57921
Deposited By: David Hall
Deposited On:01 Jun 2015 22:34
Last Modified:03 Oct 2019 08:30

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