Crowd density estimation based on classification activation map and patch density level
Abstract The task of crowd counting and density map estimation is riddled with many challenges, such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Due to the development of deep learning and large crowd datasets in recent years, most crowd count...
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| Autor*in: |
Zhu, Liping [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 32(2019), 9 vom: 03. Jan., Seite 5105-5116 |
|---|---|
| Übergeordnetes Werk: |
volume:32 ; year:2019 ; number:9 ; day:03 ; month:01 ; pages:5105-5116 |
| Links: |
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| DOI / URN: |
10.1007/s00521-018-3954-7 |
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OLC2025619855 |
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| 520 | |a Abstract The task of crowd counting and density map estimation is riddled with many challenges, such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Due to the development of deep learning and large crowd datasets in recent years, most crowd counting methods have achieved notable success. This paper aims to solve crowd density estimation problem for both sparse and dense conditions. To this end, we make two contributions: (1) a network named Patch Scale Discriminant Regression Network (PSDR). Given an input crowd image, it divides the image into patches and sends image patches of different density levels into different regression networks to get the corresponding density maps. It combines all patch density maps to predict the entire density map as the output. (2) A person classification activation map (CAM) method. CAM provides person location information and guides the generation of the entire density map in the final stage. Experiment confirms that CAM allows PSDR to gain another round of performance boost. For instance, on the SmartCity dataset, we achieve (8.6–1.1) MAE and (11.6–1.4) MSE. Our method combining above two methods performs better than state-of-the-art methods. | ||
| 650 | 4 | |a Crowd density estimation | |
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| 700 | 1 | |a Wang, Shang |4 aut | |
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10.1007/s00521-018-3954-7 doi (DE-627)OLC2025619855 (DE-He213)s00521-018-3954-7-p DE-627 ger DE-627 rakwb eng 004 VZ Zhu, Liping verfasserin aut Crowd density estimation based on classification activation map and patch density level 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract The task of crowd counting and density map estimation is riddled with many challenges, such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Due to the development of deep learning and large crowd datasets in recent years, most crowd counting methods have achieved notable success. This paper aims to solve crowd density estimation problem for both sparse and dense conditions. To this end, we make two contributions: (1) a network named Patch Scale Discriminant Regression Network (PSDR). Given an input crowd image, it divides the image into patches and sends image patches of different density levels into different regression networks to get the corresponding density maps. It combines all patch density maps to predict the entire density map as the output. (2) A person classification activation map (CAM) method. CAM provides person location information and guides the generation of the entire density map in the final stage. Experiment confirms that CAM allows PSDR to gain another round of performance boost. For instance, on the SmartCity dataset, we achieve (8.6–1.1) MAE and (11.6–1.4) MSE. Our method combining above two methods performs better than state-of-the-art methods. Crowd density estimation Image patch Density level Attention mechanism Classification activation map Li, Chengyang (orcid)0000-0002-1379-1222 aut Yang, Zhongguo aut Yuan, Kun aut Wang, Shang aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 9 vom: 03. Jan., Seite 5105-5116 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:9 day:03 month:01 pages:5105-5116 https://doi.org/10.1007/s00521-018-3954-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 9 03 01 5105-5116 |
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10.1007/s00521-018-3954-7 doi (DE-627)OLC2025619855 (DE-He213)s00521-018-3954-7-p DE-627 ger DE-627 rakwb eng 004 VZ Zhu, Liping verfasserin aut Crowd density estimation based on classification activation map and patch density level 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract The task of crowd counting and density map estimation is riddled with many challenges, such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Due to the development of deep learning and large crowd datasets in recent years, most crowd counting methods have achieved notable success. This paper aims to solve crowd density estimation problem for both sparse and dense conditions. To this end, we make two contributions: (1) a network named Patch Scale Discriminant Regression Network (PSDR). Given an input crowd image, it divides the image into patches and sends image patches of different density levels into different regression networks to get the corresponding density maps. It combines all patch density maps to predict the entire density map as the output. (2) A person classification activation map (CAM) method. CAM provides person location information and guides the generation of the entire density map in the final stage. Experiment confirms that CAM allows PSDR to gain another round of performance boost. For instance, on the SmartCity dataset, we achieve (8.6–1.1) MAE and (11.6–1.4) MSE. Our method combining above two methods performs better than state-of-the-art methods. Crowd density estimation Image patch Density level Attention mechanism Classification activation map Li, Chengyang (orcid)0000-0002-1379-1222 aut Yang, Zhongguo aut Yuan, Kun aut Wang, Shang aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 9 vom: 03. Jan., Seite 5105-5116 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:9 day:03 month:01 pages:5105-5116 https://doi.org/10.1007/s00521-018-3954-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 9 03 01 5105-5116 |
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10.1007/s00521-018-3954-7 doi (DE-627)OLC2025619855 (DE-He213)s00521-018-3954-7-p DE-627 ger DE-627 rakwb eng 004 VZ Zhu, Liping verfasserin aut Crowd density estimation based on classification activation map and patch density level 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract The task of crowd counting and density map estimation is riddled with many challenges, such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Due to the development of deep learning and large crowd datasets in recent years, most crowd counting methods have achieved notable success. This paper aims to solve crowd density estimation problem for both sparse and dense conditions. To this end, we make two contributions: (1) a network named Patch Scale Discriminant Regression Network (PSDR). Given an input crowd image, it divides the image into patches and sends image patches of different density levels into different regression networks to get the corresponding density maps. It combines all patch density maps to predict the entire density map as the output. (2) A person classification activation map (CAM) method. CAM provides person location information and guides the generation of the entire density map in the final stage. Experiment confirms that CAM allows PSDR to gain another round of performance boost. For instance, on the SmartCity dataset, we achieve (8.6–1.1) MAE and (11.6–1.4) MSE. Our method combining above two methods performs better than state-of-the-art methods. Crowd density estimation Image patch Density level Attention mechanism Classification activation map Li, Chengyang (orcid)0000-0002-1379-1222 aut Yang, Zhongguo aut Yuan, Kun aut Wang, Shang aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 9 vom: 03. Jan., Seite 5105-5116 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:9 day:03 month:01 pages:5105-5116 https://doi.org/10.1007/s00521-018-3954-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 9 03 01 5105-5116 |
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Crowd density estimation based on classification activation map and patch density level |
| abstract |
Abstract The task of crowd counting and density map estimation is riddled with many challenges, such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Due to the development of deep learning and large crowd datasets in recent years, most crowd counting methods have achieved notable success. This paper aims to solve crowd density estimation problem for both sparse and dense conditions. To this end, we make two contributions: (1) a network named Patch Scale Discriminant Regression Network (PSDR). Given an input crowd image, it divides the image into patches and sends image patches of different density levels into different regression networks to get the corresponding density maps. It combines all patch density maps to predict the entire density map as the output. (2) A person classification activation map (CAM) method. CAM provides person location information and guides the generation of the entire density map in the final stage. Experiment confirms that CAM allows PSDR to gain another round of performance boost. For instance, on the SmartCity dataset, we achieve (8.6–1.1) MAE and (11.6–1.4) MSE. Our method combining above two methods performs better than state-of-the-art methods. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| abstractGer |
Abstract The task of crowd counting and density map estimation is riddled with many challenges, such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Due to the development of deep learning and large crowd datasets in recent years, most crowd counting methods have achieved notable success. This paper aims to solve crowd density estimation problem for both sparse and dense conditions. To this end, we make two contributions: (1) a network named Patch Scale Discriminant Regression Network (PSDR). Given an input crowd image, it divides the image into patches and sends image patches of different density levels into different regression networks to get the corresponding density maps. It combines all patch density maps to predict the entire density map as the output. (2) A person classification activation map (CAM) method. CAM provides person location information and guides the generation of the entire density map in the final stage. Experiment confirms that CAM allows PSDR to gain another round of performance boost. For instance, on the SmartCity dataset, we achieve (8.6–1.1) MAE and (11.6–1.4) MSE. Our method combining above two methods performs better than state-of-the-art methods. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract The task of crowd counting and density map estimation is riddled with many challenges, such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Due to the development of deep learning and large crowd datasets in recent years, most crowd counting methods have achieved notable success. This paper aims to solve crowd density estimation problem for both sparse and dense conditions. To this end, we make two contributions: (1) a network named Patch Scale Discriminant Regression Network (PSDR). Given an input crowd image, it divides the image into patches and sends image patches of different density levels into different regression networks to get the corresponding density maps. It combines all patch density maps to predict the entire density map as the output. (2) A person classification activation map (CAM) method. CAM provides person location information and guides the generation of the entire density map in the final stage. Experiment confirms that CAM allows PSDR to gain another round of performance boost. For instance, on the SmartCity dataset, we achieve (8.6–1.1) MAE and (11.6–1.4) MSE. Our method combining above two methods performs better than state-of-the-art methods. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
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GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 |
| container_issue |
9 |
| title_short |
Crowd density estimation based on classification activation map and patch density level |
| url |
https://doi.org/10.1007/s00521-018-3954-7 |
| remote_bool |
false |
| author2 |
Li, Chengyang Yang, Zhongguo Yuan, Kun Wang, Shang |
| author2Str |
Li, Chengyang Yang, Zhongguo Yuan, Kun Wang, Shang |
| ppnlink |
165669608 |
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| hochschulschrift_bool |
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| doi_str |
10.1007/s00521-018-3954-7 |
| up_date |
2024-07-04T01:43:49.459Z |
| _version_ |
1803610939152400384 |
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| score |
7.4005365 |