Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation

Predicting future frames through image‐to‐image translation and using these synthetically generated frames for high‐speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames wa...
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Autor*in:	Md Moniruzzaman [verfasserIn] Alexander Rassau [verfasserIn] Douglas Chai [verfasserIn] Syed Mohammed Shamsul Islam [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	conditional generative adversarial networks future frame predictions perpetual losses robotic vehicles teleoperation

Übergeordnetes Werk:	In: Advanced Intelligent Systems - Wiley, 2019, 5(2023), 10, Seite n/a-n/a
Übergeordnetes Werk:	volume:5 ; year:2023 ; number:10 ; pages:n/a-n/a

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1002/aisy.202200439

Katalog-ID:	DOAJ101510454

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520			\|a Predicting future frames through image‐to‐image translation and using these synthetically generated frames for high‐speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure‐aware SSIM‐based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS‐SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of <0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model.
650		4	\|a conditional generative adversarial networks
650		4	\|a future frame predictions
650		4	\|a perpetual losses
650		4	\|a robotic vehicles
650		4	\|a teleoperation
653		0	\|a Computer engineering. Computer hardware
653		0	\|a Control engineering systems. Automatic machinery (General)
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700	0		\|a Douglas Chai \|e verfasserin \|4 aut
700	0		\|a Syed Mohammed Shamsul Islam \|e verfasserin \|4 aut
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912			\|a GBV_ILN_267
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
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allfields	10.1002/aisy.202200439 doi (DE-627)DOAJ101510454 (DE-599)DOAJbd42358471b546cb8cfd625ff51ee9a7 DE-627 ger DE-627 rakwb eng TK7885-7895 TJ212-225 Md Moniruzzaman verfasserin aut Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predicting future frames through image‐to‐image translation and using these synthetically generated frames for high‐speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure‐aware SSIM‐based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS‐SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of <0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model. conditional generative adversarial networks future frame predictions perpetual losses robotic vehicles teleoperation Computer engineering. Computer hardware Control engineering systems. Automatic machinery (General) Alexander Rassau verfasserin aut Douglas Chai verfasserin aut Syed Mohammed Shamsul Islam verfasserin aut In Advanced Intelligent Systems Wiley, 2019 5(2023), 10, Seite n/a-n/a (DE-627)166775601X (DE-600)2975566-9 26404567 nnns volume:5 year:2023 number:10 pages:n/a-n/a https://doi.org/10.1002/aisy.202200439 kostenfrei https://doaj.org/article/bd42358471b546cb8cfd625ff51ee9a7 kostenfrei https://doi.org/10.1002/aisy.202200439 kostenfrei https://doaj.org/toc/2640-4567 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2023 10 n/a-n/a
spelling	10.1002/aisy.202200439 doi (DE-627)DOAJ101510454 (DE-599)DOAJbd42358471b546cb8cfd625ff51ee9a7 DE-627 ger DE-627 rakwb eng TK7885-7895 TJ212-225 Md Moniruzzaman verfasserin aut Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predicting future frames through image‐to‐image translation and using these synthetically generated frames for high‐speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure‐aware SSIM‐based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS‐SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of <0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model. conditional generative adversarial networks future frame predictions perpetual losses robotic vehicles teleoperation Computer engineering. Computer hardware Control engineering systems. Automatic machinery (General) Alexander Rassau verfasserin aut Douglas Chai verfasserin aut Syed Mohammed Shamsul Islam verfasserin aut In Advanced Intelligent Systems Wiley, 2019 5(2023), 10, Seite n/a-n/a (DE-627)166775601X (DE-600)2975566-9 26404567 nnns volume:5 year:2023 number:10 pages:n/a-n/a https://doi.org/10.1002/aisy.202200439 kostenfrei https://doaj.org/article/bd42358471b546cb8cfd625ff51ee9a7 kostenfrei https://doi.org/10.1002/aisy.202200439 kostenfrei https://doaj.org/toc/2640-4567 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2023 10 n/a-n/a
allfields_unstemmed	10.1002/aisy.202200439 doi (DE-627)DOAJ101510454 (DE-599)DOAJbd42358471b546cb8cfd625ff51ee9a7 DE-627 ger DE-627 rakwb eng TK7885-7895 TJ212-225 Md Moniruzzaman verfasserin aut Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predicting future frames through image‐to‐image translation and using these synthetically generated frames for high‐speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure‐aware SSIM‐based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS‐SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of <0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model. conditional generative adversarial networks future frame predictions perpetual losses robotic vehicles teleoperation Computer engineering. Computer hardware Control engineering systems. Automatic machinery (General) Alexander Rassau verfasserin aut Douglas Chai verfasserin aut Syed Mohammed Shamsul Islam verfasserin aut In Advanced Intelligent Systems Wiley, 2019 5(2023), 10, Seite n/a-n/a (DE-627)166775601X (DE-600)2975566-9 26404567 nnns volume:5 year:2023 number:10 pages:n/a-n/a https://doi.org/10.1002/aisy.202200439 kostenfrei https://doaj.org/article/bd42358471b546cb8cfd625ff51ee9a7 kostenfrei https://doi.org/10.1002/aisy.202200439 kostenfrei https://doaj.org/toc/2640-4567 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2023 10 n/a-n/a
allfieldsGer	10.1002/aisy.202200439 doi (DE-627)DOAJ101510454 (DE-599)DOAJbd42358471b546cb8cfd625ff51ee9a7 DE-627 ger DE-627 rakwb eng TK7885-7895 TJ212-225 Md Moniruzzaman verfasserin aut Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predicting future frames through image‐to‐image translation and using these synthetically generated frames for high‐speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure‐aware SSIM‐based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS‐SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of <0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model. conditional generative adversarial networks future frame predictions perpetual losses robotic vehicles teleoperation Computer engineering. Computer hardware Control engineering systems. Automatic machinery (General) Alexander Rassau verfasserin aut Douglas Chai verfasserin aut Syed Mohammed Shamsul Islam verfasserin aut In Advanced Intelligent Systems Wiley, 2019 5(2023), 10, Seite n/a-n/a (DE-627)166775601X (DE-600)2975566-9 26404567 nnns volume:5 year:2023 number:10 pages:n/a-n/a https://doi.org/10.1002/aisy.202200439 kostenfrei https://doaj.org/article/bd42358471b546cb8cfd625ff51ee9a7 kostenfrei https://doi.org/10.1002/aisy.202200439 kostenfrei https://doaj.org/toc/2640-4567 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2023 10 n/a-n/a
allfieldsSound	10.1002/aisy.202200439 doi (DE-627)DOAJ101510454 (DE-599)DOAJbd42358471b546cb8cfd625ff51ee9a7 DE-627 ger DE-627 rakwb eng TK7885-7895 TJ212-225 Md Moniruzzaman verfasserin aut Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Predicting future frames through image‐to‐image translation and using these synthetically generated frames for high‐speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure‐aware SSIM‐based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS‐SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of <0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model. conditional generative adversarial networks future frame predictions perpetual losses robotic vehicles teleoperation Computer engineering. Computer hardware Control engineering systems. Automatic machinery (General) Alexander Rassau verfasserin aut Douglas Chai verfasserin aut Syed Mohammed Shamsul Islam verfasserin aut In Advanced Intelligent Systems Wiley, 2019 5(2023), 10, Seite n/a-n/a (DE-627)166775601X (DE-600)2975566-9 26404567 nnns volume:5 year:2023 number:10 pages:n/a-n/a https://doi.org/10.1002/aisy.202200439 kostenfrei https://doaj.org/article/bd42358471b546cb8cfd625ff51ee9a7 kostenfrei https://doi.org/10.1002/aisy.202200439 kostenfrei https://doaj.org/toc/2640-4567 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2023 10 n/a-n/a
language	English
source	In Advanced Intelligent Systems 5(2023), 10, Seite n/a-n/a volume:5 year:2023 number:10 pages:n/a-n/a
sourceStr	In Advanced Intelligent Systems 5(2023), 10, Seite n/a-n/a volume:5 year:2023 number:10 pages:n/a-n/a
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	conditional generative adversarial networks future frame predictions perpetual losses robotic vehicles teleoperation Computer engineering. Computer hardware Control engineering systems. Automatic machinery (General)
isfreeaccess_bool	true
container_title	Advanced Intelligent Systems
authorswithroles_txt_mv	Md Moniruzzaman @@aut@@ Alexander Rassau @@aut@@ Douglas Chai @@aut@@ Syed Mohammed Shamsul Islam @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	166775601X
id	DOAJ101510454
language_de	englisch
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callnumber-first	T - Technology
author	Md Moniruzzaman
spellingShingle	Md Moniruzzaman misc TK7885-7895 misc TJ212-225 misc conditional generative adversarial networks misc future frame predictions misc perpetual losses misc robotic vehicles misc teleoperation misc Computer engineering. Computer hardware misc Control engineering systems. Automatic machinery (General) Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation
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topic_title	TK7885-7895 TJ212-225 Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation conditional generative adversarial networks future frame predictions perpetual losses robotic vehicles teleoperation
topic	misc TK7885-7895 misc TJ212-225 misc conditional generative adversarial networks misc future frame predictions misc perpetual losses misc robotic vehicles misc teleoperation misc Computer engineering. Computer hardware misc Control engineering systems. Automatic machinery (General)
topic_unstemmed	misc TK7885-7895 misc TJ212-225 misc conditional generative adversarial networks misc future frame predictions misc perpetual losses misc robotic vehicles misc teleoperation misc Computer engineering. Computer hardware misc Control engineering systems. Automatic machinery (General)
topic_browse	misc TK7885-7895 misc TJ212-225 misc conditional generative adversarial networks misc future frame predictions misc perpetual losses misc robotic vehicles misc teleoperation misc Computer engineering. Computer hardware misc Control engineering systems. Automatic machinery (General)
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title	Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation
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title_sort	structure‐aware image translation‐based long future prediction for enhancement of ground robotic vehicle teleoperation
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title_auth	Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation
abstract	Predicting future frames through image‐to‐image translation and using these synthetically generated frames for high‐speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure‐aware SSIM‐based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS‐SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of <0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model.
abstractGer	Predicting future frames through image‐to‐image translation and using these synthetically generated frames for high‐speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure‐aware SSIM‐based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS‐SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of <0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model.
abstract_unstemmed	Predicting future frames through image‐to‐image translation and using these synthetically generated frames for high‐speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure‐aware SSIM‐based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS‐SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of <0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model.
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title_short	Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation
url	https://doi.org/10.1002/aisy.202200439 https://doaj.org/article/bd42358471b546cb8cfd625ff51ee9a7 https://doaj.org/toc/2640-4567
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In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure‐aware SSIM‐based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS‐SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of <0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">conditional generative adversarial networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">future frame predictions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">perpetual losses</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">robotic vehicles</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">teleoperation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computer engineering. Computer hardware</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Control engineering systems. Automatic machinery (General)</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Alexander Rassau</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Douglas Chai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Syed Mohammed Shamsul Islam</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Advanced Intelligent Systems</subfield><subfield code="d">Wiley, 2019</subfield><subfield code="g">5(2023), 10, Seite n/a-n/a</subfield><subfield code="w">(DE-627)166775601X</subfield><subfield code="w">(DE-600)2975566-9</subfield><subfield code="x">26404567</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" 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Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation

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