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Conference Paper: Deep learning strong parts for pedestrian detection

TitleDeep learning strong parts for pedestrian detection
Authors
Issue Date2015
Citation
Proceedings of the IEEE International Conference on Computer Vision, 2015, v. 2015 International Conference on Computer Vision, ICCV 2015, p. 1904-1912 How to Cite?
Abstract© 2015 IEEE. Recent advances in pedestrian detection are attained by transferring the learned features of Convolutional Neural Network (ConvNet) to pedestrians. This ConvNet is typically pre-trained with massive general object categories (e.g. ImageNet). Although these features are able to handle variations such as poses, viewpoints, and lightings, they may fail when pedestrian images with complex occlusions are present. Occlusion handling is one of the most important problem in pedestrian detection. Unlike previous deep models that directly learned a single detector for pedestrian detection, we propose DeepParts, which consists of extensive part detectors. DeepParts has several appealing properties. First, DeepParts can be trained on weakly labeled data, i.e. only pedestrian bounding boxes without part annotations are provided. Second, DeepParts is able to handle low IoU positive proposals that shift away from ground truth. Third, each part detector in DeepParts is a strong detector that can detect pedestrian by observing only a part of a proposal. Extensive experiments in Caltech dataset demonstrate the effectiveness of DeepParts, which yields a new state-of-the-art miss rate of 11:89%, outperforming the second best method by 10%.
Persistent Identifierhttp://hdl.handle.net/10722/273718
ISSN

 

DC FieldValueLanguage
dc.contributor.authorTian, Yonglong-
dc.contributor.authorLuo, Ping-
dc.contributor.authorWang, Xiaogang-
dc.contributor.authorTang, Xiaoou-
dc.date.accessioned2019-08-12T09:56:27Z-
dc.date.available2019-08-12T09:56:27Z-
dc.date.issued2015-
dc.identifier.citationProceedings of the IEEE International Conference on Computer Vision, 2015, v. 2015 International Conference on Computer Vision, ICCV 2015, p. 1904-1912-
dc.identifier.issn1550-5499-
dc.identifier.urihttp://hdl.handle.net/10722/273718-
dc.description.abstract© 2015 IEEE. Recent advances in pedestrian detection are attained by transferring the learned features of Convolutional Neural Network (ConvNet) to pedestrians. This ConvNet is typically pre-trained with massive general object categories (e.g. ImageNet). Although these features are able to handle variations such as poses, viewpoints, and lightings, they may fail when pedestrian images with complex occlusions are present. Occlusion handling is one of the most important problem in pedestrian detection. Unlike previous deep models that directly learned a single detector for pedestrian detection, we propose DeepParts, which consists of extensive part detectors. DeepParts has several appealing properties. First, DeepParts can be trained on weakly labeled data, i.e. only pedestrian bounding boxes without part annotations are provided. Second, DeepParts is able to handle low IoU positive proposals that shift away from ground truth. Third, each part detector in DeepParts is a strong detector that can detect pedestrian by observing only a part of a proposal. Extensive experiments in Caltech dataset demonstrate the effectiveness of DeepParts, which yields a new state-of-the-art miss rate of 11:89%, outperforming the second best method by 10%.-
dc.languageeng-
dc.relation.ispartofProceedings of the IEEE International Conference on Computer Vision-
dc.titleDeep learning strong parts for pedestrian detection-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICCV.2015.221-
dc.identifier.scopuseid_2-s2.0-84973883645-
dc.identifier.volume2015 International Conference on Computer Vision, ICCV 2015-
dc.identifier.spage1904-
dc.identifier.epage1912-

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