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Mask-Guided Attenti...
Mask-Guided Attention Network for Occluded Pedestrian Detection
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- Pang, Yanwei (författare)
- Tianjin Univ, Peoples R China
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- Xie, Jin (författare)
- Tianjin Univ, Peoples R China
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- Khan, Muhammad Haris (författare)
- Incept Inst Artificial Intelligence, U Arab Emirates
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- Anwer, Rao Muhammad (författare)
- Incept Inst Artificial Intelligence, U Arab Emirates
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- Khan, Fahad Shahbaz, 1983- (författare)
- Linköpings universitet,Datorseende,Tekniska fakulteten,Incept Inst Artificial Intelligence, U Arab Emirates
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- Shao, Ling (författare)
- Incept Inst Artificial Intelligence, U Arab Emirates
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(creator_code:org_t)
- IEEE COMPUTER SOC, 2019
- 2019
- Engelska.
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Ingår i: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019). - : IEEE COMPUTER SOC. - 9781728148038 ; , s. 4966-4974
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance on heavily occluded pedestrians remains far from satisfactory. The main culprits are intra-class occlusions involving other pedestrians and inter-class occlusions caused by other objects, such as cars and bicycles. These result in a multitude of occlusion patterns. We propose an approach for occluded pedestrian detection with the following contributions. First, we introduce a novel mask-guided attention network that fits naturally into popular pedestrian detection pipelines. Our attention network emphasizes on visible pedestrian regions while suppressing the occluded ones by modulating full body features. Second, we empirically demonstrate that coarse-level segmentation annotations provide reasonable approximation to their dense pixel-wise counterparts. Experiments are performed on CityPersons and Caltech datasets. Our approach sets a new state-of-the-art on both datasets. Our approach obtains an absolute gain of 9.5% in log-average miss rate, compared to the best reported results [31] on the heavily occluded HO pedestrian set of CityPersons test set. Further, on the HO pedestrian set of Caltech dataset, our method achieves an absolute gain of 5.0% in log-average miss rate, compared to the best reported results [13]. Code and models are available at: https://github.com/Leotju/MGAN.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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