A Comparison of Bottom-Up Models for Spatial Saliency Predictions in Autonomous Driving

Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we conduct a systematic evaluation of the saliency maps...
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Gespeichert in:

Autor*in:	Jaime Maldonado [verfasserIn] Lino Antoni Giefer [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	autonomous driving bottom-up saliency models perception saliency detection saliency maps visual salience

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 21(2021), 20, p 6825
Übergeordnetes Werk:	volume:21 ; year:2021 ; number:20, p 6825

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s21206825

Katalog-ID:	DOAJ024021180

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spelling	10.3390/s21206825 doi (DE-627)DOAJ024021180 (DE-599)DOAJ9c6a9c519e4e4f7296c73bb07d3ba6d5 DE-627 ger DE-627 rakwb eng TP1-1185 Jaime Maldonado verfasserin aut A Comparison of Bottom-Up Models for Spatial Saliency Predictions in Autonomous Driving 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we conduct a systematic evaluation of the saliency maps computed with four selected bottom-up models on images of urban and highway traffic scenes. Saliency both over whole images and on object level is investigated and elaborated in terms of the energy and the entropy of the saliency maps. We identify significant differences with respect to the amount, size and shape-complexity of the salient areas computed by different models. Based on these findings, we analyze the likelihood that object instances fall within the salient areas of an image and investigate the agreement between the segments of traffic participants and the saliency maps of the different models. The overall and object-level analysis provides insights on the distinctive features of salient areas identified by different models, which can be used as selection criteria for prospective applications in autonomous driving such as object detection and tracking. autonomous driving bottom-up saliency models perception saliency detection saliency maps visual salience Chemical technology Lino Antoni Giefer verfasserin aut In Sensors MDPI AG, 2003 21(2021), 20, p 6825 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:20, p 6825 https://doi.org/10.3390/s21206825 kostenfrei https://doaj.org/article/9c6a9c519e4e4f7296c73bb07d3ba6d5 kostenfrei https://www.mdpi.com/1424-8220/21/20/6825 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 20, p 6825
allfields_unstemmed	10.3390/s21206825 doi (DE-627)DOAJ024021180 (DE-599)DOAJ9c6a9c519e4e4f7296c73bb07d3ba6d5 DE-627 ger DE-627 rakwb eng TP1-1185 Jaime Maldonado verfasserin aut A Comparison of Bottom-Up Models for Spatial Saliency Predictions in Autonomous Driving 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we conduct a systematic evaluation of the saliency maps computed with four selected bottom-up models on images of urban and highway traffic scenes. Saliency both over whole images and on object level is investigated and elaborated in terms of the energy and the entropy of the saliency maps. We identify significant differences with respect to the amount, size and shape-complexity of the salient areas computed by different models. Based on these findings, we analyze the likelihood that object instances fall within the salient areas of an image and investigate the agreement between the segments of traffic participants and the saliency maps of the different models. The overall and object-level analysis provides insights on the distinctive features of salient areas identified by different models, which can be used as selection criteria for prospective applications in autonomous driving such as object detection and tracking. autonomous driving bottom-up saliency models perception saliency detection saliency maps visual salience Chemical technology Lino Antoni Giefer verfasserin aut In Sensors MDPI AG, 2003 21(2021), 20, p 6825 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:20, p 6825 https://doi.org/10.3390/s21206825 kostenfrei https://doaj.org/article/9c6a9c519e4e4f7296c73bb07d3ba6d5 kostenfrei https://www.mdpi.com/1424-8220/21/20/6825 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 20, p 6825
allfieldsGer	10.3390/s21206825 doi (DE-627)DOAJ024021180 (DE-599)DOAJ9c6a9c519e4e4f7296c73bb07d3ba6d5 DE-627 ger DE-627 rakwb eng TP1-1185 Jaime Maldonado verfasserin aut A Comparison of Bottom-Up Models for Spatial Saliency Predictions in Autonomous Driving 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we conduct a systematic evaluation of the saliency maps computed with four selected bottom-up models on images of urban and highway traffic scenes. Saliency both over whole images and on object level is investigated and elaborated in terms of the energy and the entropy of the saliency maps. We identify significant differences with respect to the amount, size and shape-complexity of the salient areas computed by different models. Based on these findings, we analyze the likelihood that object instances fall within the salient areas of an image and investigate the agreement between the segments of traffic participants and the saliency maps of the different models. The overall and object-level analysis provides insights on the distinctive features of salient areas identified by different models, which can be used as selection criteria for prospective applications in autonomous driving such as object detection and tracking. autonomous driving bottom-up saliency models perception saliency detection saliency maps visual salience Chemical technology Lino Antoni Giefer verfasserin aut In Sensors MDPI AG, 2003 21(2021), 20, p 6825 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:20, p 6825 https://doi.org/10.3390/s21206825 kostenfrei https://doaj.org/article/9c6a9c519e4e4f7296c73bb07d3ba6d5 kostenfrei https://www.mdpi.com/1424-8220/21/20/6825 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 20, p 6825
allfieldsSound	10.3390/s21206825 doi (DE-627)DOAJ024021180 (DE-599)DOAJ9c6a9c519e4e4f7296c73bb07d3ba6d5 DE-627 ger DE-627 rakwb eng TP1-1185 Jaime Maldonado verfasserin aut A Comparison of Bottom-Up Models for Spatial Saliency Predictions in Autonomous Driving 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we conduct a systematic evaluation of the saliency maps computed with four selected bottom-up models on images of urban and highway traffic scenes. Saliency both over whole images and on object level is investigated and elaborated in terms of the energy and the entropy of the saliency maps. We identify significant differences with respect to the amount, size and shape-complexity of the salient areas computed by different models. Based on these findings, we analyze the likelihood that object instances fall within the salient areas of an image and investigate the agreement between the segments of traffic participants and the saliency maps of the different models. The overall and object-level analysis provides insights on the distinctive features of salient areas identified by different models, which can be used as selection criteria for prospective applications in autonomous driving such as object detection and tracking. autonomous driving bottom-up saliency models perception saliency detection saliency maps visual salience Chemical technology Lino Antoni Giefer verfasserin aut In Sensors MDPI AG, 2003 21(2021), 20, p 6825 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:20, p 6825 https://doi.org/10.3390/s21206825 kostenfrei https://doaj.org/article/9c6a9c519e4e4f7296c73bb07d3ba6d5 kostenfrei https://www.mdpi.com/1424-8220/21/20/6825 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 20, p 6825
language	English
source	In Sensors 21(2021), 20, p 6825 volume:21 year:2021 number:20, p 6825
sourceStr	In Sensors 21(2021), 20, p 6825 volume:21 year:2021 number:20, p 6825
format_phy_str_mv	Article
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topic_facet	autonomous driving bottom-up saliency models perception saliency detection saliency maps visual salience Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Jaime Maldonado @@aut@@ Lino Antoni Giefer @@aut@@
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callnumber-first	T - Technology
author	Jaime Maldonado
spellingShingle	Jaime Maldonado misc TP1-1185 misc autonomous driving misc bottom-up saliency models misc perception misc saliency detection misc saliency maps misc visual salience misc Chemical technology A Comparison of Bottom-Up Models for Spatial Saliency Predictions in Autonomous Driving
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topic_title	TP1-1185 A Comparison of Bottom-Up Models for Spatial Saliency Predictions in Autonomous Driving autonomous driving bottom-up saliency models perception saliency detection saliency maps visual salience
topic	misc TP1-1185 misc autonomous driving misc bottom-up saliency models misc perception misc saliency detection misc saliency maps misc visual salience misc Chemical technology
topic_unstemmed	misc TP1-1185 misc autonomous driving misc bottom-up saliency models misc perception misc saliency detection misc saliency maps misc visual salience misc Chemical technology
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title	A Comparison of Bottom-Up Models for Spatial Saliency Predictions in Autonomous Driving
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title_sort	comparison of bottom-up models for spatial saliency predictions in autonomous driving
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title_auth	A Comparison of Bottom-Up Models for Spatial Saliency Predictions in Autonomous Driving
abstract	Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we conduct a systematic evaluation of the saliency maps computed with four selected bottom-up models on images of urban and highway traffic scenes. Saliency both over whole images and on object level is investigated and elaborated in terms of the energy and the entropy of the saliency maps. We identify significant differences with respect to the amount, size and shape-complexity of the salient areas computed by different models. Based on these findings, we analyze the likelihood that object instances fall within the salient areas of an image and investigate the agreement between the segments of traffic participants and the saliency maps of the different models. The overall and object-level analysis provides insights on the distinctive features of salient areas identified by different models, which can be used as selection criteria for prospective applications in autonomous driving such as object detection and tracking.
abstractGer	Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we conduct a systematic evaluation of the saliency maps computed with four selected bottom-up models on images of urban and highway traffic scenes. Saliency both over whole images and on object level is investigated and elaborated in terms of the energy and the entropy of the saliency maps. We identify significant differences with respect to the amount, size and shape-complexity of the salient areas computed by different models. Based on these findings, we analyze the likelihood that object instances fall within the salient areas of an image and investigate the agreement between the segments of traffic participants and the saliency maps of the different models. The overall and object-level analysis provides insights on the distinctive features of salient areas identified by different models, which can be used as selection criteria for prospective applications in autonomous driving such as object detection and tracking.
abstract_unstemmed	Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we conduct a systematic evaluation of the saliency maps computed with four selected bottom-up models on images of urban and highway traffic scenes. Saliency both over whole images and on object level is investigated and elaborated in terms of the energy and the entropy of the saliency maps. We identify significant differences with respect to the amount, size and shape-complexity of the salient areas computed by different models. Based on these findings, we analyze the likelihood that object instances fall within the salient areas of an image and investigate the agreement between the segments of traffic participants and the saliency maps of the different models. The overall and object-level analysis provides insights on the distinctive features of salient areas identified by different models, which can be used as selection criteria for prospective applications in autonomous driving such as object detection and tracking.
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title_short	A Comparison of Bottom-Up Models for Spatial Saliency Predictions in Autonomous Driving
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