Segmentation of Pedestrians with Confidence Level Computation
Abstract In this work, we propose a mechanism to segment groups of pedestrians with confidence level computation for intelligent surveillance systems. The goal is to specify the number of people and locate the position and size of each individual in groups of people. Human detection and clustering t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cheng, Hsu-Yung [verfasserIn] Zeng, You-Jhen [verfasserIn] Lee, Chien-Cheng [verfasserIn] Hsu, Shih-Han [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of VLSI signal processing systems for signal, image and video technology - Springer Netherlands, 1989, 72(2012), 2 vom: 17. Okt., Seite 87-97 |
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| Übergeordnetes Werk: |
volume:72 ; year:2012 ; number:2 ; day:17 ; month:10 ; pages:87-97 |
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| DOI / URN: |
10.1007/s11265-012-0708-0 |
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SPR018326331 |
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| 520 | |a Abstract In this work, we propose a mechanism to segment groups of pedestrians with confidence level computation for intelligent surveillance systems. The goal is to specify the number of people and locate the position and size of each individual in groups of people. Human detection and clustering techniques are combined to achieve the segmentation purpose. The histogram of oriented gradients and curvelet features are extracted for full body detection using a support vector machine classifier. Modified Haar of Oriented Gradient features are constructed for upper body and lower body detectors. A clustering algorithm is then applied to the detected humans to eliminate the redundant detection responses. The proposed mechanism requires no prior assumptions of human sizes, human heights, camera distances, and other calibration parameters. Moreover, confidence level computation can provide valuable information for subsequent surveillance applications. The proposed approach is tested with pedestrian benchmark dataset and surveillance videos. The experimental results have demonstrated the effectiveness of the proposed pedestrian segmentation mechanism. | ||
| 650 | 4 | |a Pedestrian segmentation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Intelligent surveillance |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Human detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Curvelet |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Histogram of oriented gradients |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Modified Haar of oriented gradients |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Lee, Chien-Cheng |e verfasserin |4 aut | |
| 700 | 1 | |a Hsu, Shih-Han |e verfasserin |4 aut | |
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10.1007/s11265-012-0708-0 doi (DE-627)SPR018326331 (SPR)s11265-012-0708-0-e DE-627 ger DE-627 rakwb eng Cheng, Hsu-Yung verfasserin aut Segmentation of Pedestrians with Confidence Level Computation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this work, we propose a mechanism to segment groups of pedestrians with confidence level computation for intelligent surveillance systems. The goal is to specify the number of people and locate the position and size of each individual in groups of people. Human detection and clustering techniques are combined to achieve the segmentation purpose. The histogram of oriented gradients and curvelet features are extracted for full body detection using a support vector machine classifier. Modified Haar of Oriented Gradient features are constructed for upper body and lower body detectors. A clustering algorithm is then applied to the detected humans to eliminate the redundant detection responses. The proposed mechanism requires no prior assumptions of human sizes, human heights, camera distances, and other calibration parameters. Moreover, confidence level computation can provide valuable information for subsequent surveillance applications. The proposed approach is tested with pedestrian benchmark dataset and surveillance videos. The experimental results have demonstrated the effectiveness of the proposed pedestrian segmentation mechanism. Pedestrian segmentation (dpeaa)DE-He213 Intelligent surveillance (dpeaa)DE-He213 Human detection (dpeaa)DE-He213 Curvelet (dpeaa)DE-He213 Histogram of oriented gradients (dpeaa)DE-He213 Modified Haar of oriented gradients (dpeaa)DE-He213 Zeng, You-Jhen verfasserin aut Lee, Chien-Cheng verfasserin aut Hsu, Shih-Han verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 72(2012), 2 vom: 17. Okt., Seite 87-97 (DE-627)SPR018308090 nnns volume:72 year:2012 number:2 day:17 month:10 pages:87-97 https://dx.doi.org/10.1007/s11265-012-0708-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 72 2012 2 17 10 87-97 |
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10.1007/s11265-012-0708-0 doi (DE-627)SPR018326331 (SPR)s11265-012-0708-0-e DE-627 ger DE-627 rakwb eng Cheng, Hsu-Yung verfasserin aut Segmentation of Pedestrians with Confidence Level Computation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this work, we propose a mechanism to segment groups of pedestrians with confidence level computation for intelligent surveillance systems. The goal is to specify the number of people and locate the position and size of each individual in groups of people. Human detection and clustering techniques are combined to achieve the segmentation purpose. The histogram of oriented gradients and curvelet features are extracted for full body detection using a support vector machine classifier. Modified Haar of Oriented Gradient features are constructed for upper body and lower body detectors. A clustering algorithm is then applied to the detected humans to eliminate the redundant detection responses. The proposed mechanism requires no prior assumptions of human sizes, human heights, camera distances, and other calibration parameters. Moreover, confidence level computation can provide valuable information for subsequent surveillance applications. The proposed approach is tested with pedestrian benchmark dataset and surveillance videos. The experimental results have demonstrated the effectiveness of the proposed pedestrian segmentation mechanism. Pedestrian segmentation (dpeaa)DE-He213 Intelligent surveillance (dpeaa)DE-He213 Human detection (dpeaa)DE-He213 Curvelet (dpeaa)DE-He213 Histogram of oriented gradients (dpeaa)DE-He213 Modified Haar of oriented gradients (dpeaa)DE-He213 Zeng, You-Jhen verfasserin aut Lee, Chien-Cheng verfasserin aut Hsu, Shih-Han verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 72(2012), 2 vom: 17. Okt., Seite 87-97 (DE-627)SPR018308090 nnns volume:72 year:2012 number:2 day:17 month:10 pages:87-97 https://dx.doi.org/10.1007/s11265-012-0708-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 72 2012 2 17 10 87-97 |
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10.1007/s11265-012-0708-0 doi (DE-627)SPR018326331 (SPR)s11265-012-0708-0-e DE-627 ger DE-627 rakwb eng Cheng, Hsu-Yung verfasserin aut Segmentation of Pedestrians with Confidence Level Computation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this work, we propose a mechanism to segment groups of pedestrians with confidence level computation for intelligent surveillance systems. The goal is to specify the number of people and locate the position and size of each individual in groups of people. Human detection and clustering techniques are combined to achieve the segmentation purpose. The histogram of oriented gradients and curvelet features are extracted for full body detection using a support vector machine classifier. Modified Haar of Oriented Gradient features are constructed for upper body and lower body detectors. A clustering algorithm is then applied to the detected humans to eliminate the redundant detection responses. The proposed mechanism requires no prior assumptions of human sizes, human heights, camera distances, and other calibration parameters. Moreover, confidence level computation can provide valuable information for subsequent surveillance applications. The proposed approach is tested with pedestrian benchmark dataset and surveillance videos. The experimental results have demonstrated the effectiveness of the proposed pedestrian segmentation mechanism. Pedestrian segmentation (dpeaa)DE-He213 Intelligent surveillance (dpeaa)DE-He213 Human detection (dpeaa)DE-He213 Curvelet (dpeaa)DE-He213 Histogram of oriented gradients (dpeaa)DE-He213 Modified Haar of oriented gradients (dpeaa)DE-He213 Zeng, You-Jhen verfasserin aut Lee, Chien-Cheng verfasserin aut Hsu, Shih-Han verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 72(2012), 2 vom: 17. Okt., Seite 87-97 (DE-627)SPR018308090 nnns volume:72 year:2012 number:2 day:17 month:10 pages:87-97 https://dx.doi.org/10.1007/s11265-012-0708-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 72 2012 2 17 10 87-97 |
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10.1007/s11265-012-0708-0 doi (DE-627)SPR018326331 (SPR)s11265-012-0708-0-e DE-627 ger DE-627 rakwb eng Cheng, Hsu-Yung verfasserin aut Segmentation of Pedestrians with Confidence Level Computation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this work, we propose a mechanism to segment groups of pedestrians with confidence level computation for intelligent surveillance systems. The goal is to specify the number of people and locate the position and size of each individual in groups of people. Human detection and clustering techniques are combined to achieve the segmentation purpose. The histogram of oriented gradients and curvelet features are extracted for full body detection using a support vector machine classifier. Modified Haar of Oriented Gradient features are constructed for upper body and lower body detectors. A clustering algorithm is then applied to the detected humans to eliminate the redundant detection responses. The proposed mechanism requires no prior assumptions of human sizes, human heights, camera distances, and other calibration parameters. Moreover, confidence level computation can provide valuable information for subsequent surveillance applications. The proposed approach is tested with pedestrian benchmark dataset and surveillance videos. The experimental results have demonstrated the effectiveness of the proposed pedestrian segmentation mechanism. Pedestrian segmentation (dpeaa)DE-He213 Intelligent surveillance (dpeaa)DE-He213 Human detection (dpeaa)DE-He213 Curvelet (dpeaa)DE-He213 Histogram of oriented gradients (dpeaa)DE-He213 Modified Haar of oriented gradients (dpeaa)DE-He213 Zeng, You-Jhen verfasserin aut Lee, Chien-Cheng verfasserin aut Hsu, Shih-Han verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 72(2012), 2 vom: 17. Okt., Seite 87-97 (DE-627)SPR018308090 nnns volume:72 year:2012 number:2 day:17 month:10 pages:87-97 https://dx.doi.org/10.1007/s11265-012-0708-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 72 2012 2 17 10 87-97 |
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10.1007/s11265-012-0708-0 doi (DE-627)SPR018326331 (SPR)s11265-012-0708-0-e DE-627 ger DE-627 rakwb eng Cheng, Hsu-Yung verfasserin aut Segmentation of Pedestrians with Confidence Level Computation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this work, we propose a mechanism to segment groups of pedestrians with confidence level computation for intelligent surveillance systems. The goal is to specify the number of people and locate the position and size of each individual in groups of people. Human detection and clustering techniques are combined to achieve the segmentation purpose. The histogram of oriented gradients and curvelet features are extracted for full body detection using a support vector machine classifier. Modified Haar of Oriented Gradient features are constructed for upper body and lower body detectors. A clustering algorithm is then applied to the detected humans to eliminate the redundant detection responses. The proposed mechanism requires no prior assumptions of human sizes, human heights, camera distances, and other calibration parameters. Moreover, confidence level computation can provide valuable information for subsequent surveillance applications. The proposed approach is tested with pedestrian benchmark dataset and surveillance videos. The experimental results have demonstrated the effectiveness of the proposed pedestrian segmentation mechanism. Pedestrian segmentation (dpeaa)DE-He213 Intelligent surveillance (dpeaa)DE-He213 Human detection (dpeaa)DE-He213 Curvelet (dpeaa)DE-He213 Histogram of oriented gradients (dpeaa)DE-He213 Modified Haar of oriented gradients (dpeaa)DE-He213 Zeng, You-Jhen verfasserin aut Lee, Chien-Cheng verfasserin aut Hsu, Shih-Han verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 72(2012), 2 vom: 17. Okt., Seite 87-97 (DE-627)SPR018308090 nnns volume:72 year:2012 number:2 day:17 month:10 pages:87-97 https://dx.doi.org/10.1007/s11265-012-0708-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 72 2012 2 17 10 87-97 |
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Segmentation of Pedestrians with Confidence Level Computation |
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Abstract In this work, we propose a mechanism to segment groups of pedestrians with confidence level computation for intelligent surveillance systems. The goal is to specify the number of people and locate the position and size of each individual in groups of people. Human detection and clustering techniques are combined to achieve the segmentation purpose. The histogram of oriented gradients and curvelet features are extracted for full body detection using a support vector machine classifier. Modified Haar of Oriented Gradient features are constructed for upper body and lower body detectors. A clustering algorithm is then applied to the detected humans to eliminate the redundant detection responses. The proposed mechanism requires no prior assumptions of human sizes, human heights, camera distances, and other calibration parameters. Moreover, confidence level computation can provide valuable information for subsequent surveillance applications. The proposed approach is tested with pedestrian benchmark dataset and surveillance videos. The experimental results have demonstrated the effectiveness of the proposed pedestrian segmentation mechanism. |
| abstractGer |
Abstract In this work, we propose a mechanism to segment groups of pedestrians with confidence level computation for intelligent surveillance systems. The goal is to specify the number of people and locate the position and size of each individual in groups of people. Human detection and clustering techniques are combined to achieve the segmentation purpose. The histogram of oriented gradients and curvelet features are extracted for full body detection using a support vector machine classifier. Modified Haar of Oriented Gradient features are constructed for upper body and lower body detectors. A clustering algorithm is then applied to the detected humans to eliminate the redundant detection responses. The proposed mechanism requires no prior assumptions of human sizes, human heights, camera distances, and other calibration parameters. Moreover, confidence level computation can provide valuable information for subsequent surveillance applications. The proposed approach is tested with pedestrian benchmark dataset and surveillance videos. The experimental results have demonstrated the effectiveness of the proposed pedestrian segmentation mechanism. |
| abstract_unstemmed |
Abstract In this work, we propose a mechanism to segment groups of pedestrians with confidence level computation for intelligent surveillance systems. The goal is to specify the number of people and locate the position and size of each individual in groups of people. Human detection and clustering techniques are combined to achieve the segmentation purpose. The histogram of oriented gradients and curvelet features are extracted for full body detection using a support vector machine classifier. Modified Haar of Oriented Gradient features are constructed for upper body and lower body detectors. A clustering algorithm is then applied to the detected humans to eliminate the redundant detection responses. The proposed mechanism requires no prior assumptions of human sizes, human heights, camera distances, and other calibration parameters. Moreover, confidence level computation can provide valuable information for subsequent surveillance applications. The proposed approach is tested with pedestrian benchmark dataset and surveillance videos. The experimental results have demonstrated the effectiveness of the proposed pedestrian segmentation mechanism. |
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| title_short |
Segmentation of Pedestrians with Confidence Level Computation |
| url |
https://dx.doi.org/10.1007/s11265-012-0708-0 |
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| author2 |
Zeng, You-Jhen Lee, Chien-Cheng Hsu, Shih-Han |
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Zeng, You-Jhen Lee, Chien-Cheng Hsu, Shih-Han |
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| doi_str |
10.1007/s11265-012-0708-0 |
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2024-07-03T18:55:17.796Z |
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