Video Object Detection Guided by Object Blur Evaluation

In recent years, the excellent image-based object detection algorithms are transferred to the video object detection directly. These frame-by-frame processing methods are suboptimal owing to the degenerate object appearance such as motion blur, defocus and rare poses. The existing works for video ob...
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Autor*in:	Yujie Wu [verfasserIn] Hong Zhang [verfasserIn] Yawei Li [verfasserIn] Yifan Yang [verfasserIn] Ding Yuan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Video object detection object blur degree evaluation saliency detection

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 208554-208565
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:208554-208565

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3038913

Katalog-ID:	DOAJ084944153

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spelling	10.1109/ACCESS.2020.3038913 doi (DE-627)DOAJ084944153 (DE-599)DOAJ4a9efebf8e7a4ed0a366acb886bbf067 DE-627 ger DE-627 rakwb eng TK1-9971 Yujie Wu verfasserin aut Video Object Detection Guided by Object Blur Evaluation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, the excellent image-based object detection algorithms are transferred to the video object detection directly. These frame-by-frame processing methods are suboptimal owing to the degenerate object appearance such as motion blur, defocus and rare poses. The existing works for video object detection mostly focus on the feature aggregation at pixel level and instance level, but the blur impact in the aggregation process has not been exploited well so far. In this article, we propose an end-to-end blur-aid feature aggregation network (BFAN) for video object detection. The proposed BFAN focuses on the aggregation process influenced by the blur including motion blur and defocus with high accuracy and little increased computation. In BFAN, we evaluate the object blur degree of each frame as the weight for aggregation. Noteworthy, the background is usually flat which has a negative impact on the object blur degree evaluation. Therefore, we introduce a light saliency detection network to alleviate the background interference. The experiments conducted on the ImageNet VID dataset show that BFAN achieves the state-of-the-art detection performance, exactly 79.1% mAP, with 3 points improvement compared to the video object detection baseline. Video object detection object blur degree evaluation saliency detection Electrical engineering. Electronics. Nuclear engineering Hong Zhang verfasserin aut Yawei Li verfasserin aut Yifan Yang verfasserin aut Ding Yuan verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 208554-208565 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:208554-208565 https://doi.org/10.1109/ACCESS.2020.3038913 kostenfrei https://doaj.org/article/4a9efebf8e7a4ed0a366acb886bbf067 kostenfrei https://ieeexplore.ieee.org/document/9262895/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 8 2020 208554-208565
allfields_unstemmed	10.1109/ACCESS.2020.3038913 doi (DE-627)DOAJ084944153 (DE-599)DOAJ4a9efebf8e7a4ed0a366acb886bbf067 DE-627 ger DE-627 rakwb eng TK1-9971 Yujie Wu verfasserin aut Video Object Detection Guided by Object Blur Evaluation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, the excellent image-based object detection algorithms are transferred to the video object detection directly. These frame-by-frame processing methods are suboptimal owing to the degenerate object appearance such as motion blur, defocus and rare poses. The existing works for video object detection mostly focus on the feature aggregation at pixel level and instance level, but the blur impact in the aggregation process has not been exploited well so far. In this article, we propose an end-to-end blur-aid feature aggregation network (BFAN) for video object detection. The proposed BFAN focuses on the aggregation process influenced by the blur including motion blur and defocus with high accuracy and little increased computation. In BFAN, we evaluate the object blur degree of each frame as the weight for aggregation. Noteworthy, the background is usually flat which has a negative impact on the object blur degree evaluation. Therefore, we introduce a light saliency detection network to alleviate the background interference. The experiments conducted on the ImageNet VID dataset show that BFAN achieves the state-of-the-art detection performance, exactly 79.1% mAP, with 3 points improvement compared to the video object detection baseline. Video object detection object blur degree evaluation saliency detection Electrical engineering. Electronics. Nuclear engineering Hong Zhang verfasserin aut Yawei Li verfasserin aut Yifan Yang verfasserin aut Ding Yuan verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 208554-208565 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:208554-208565 https://doi.org/10.1109/ACCESS.2020.3038913 kostenfrei https://doaj.org/article/4a9efebf8e7a4ed0a366acb886bbf067 kostenfrei https://ieeexplore.ieee.org/document/9262895/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 8 2020 208554-208565
allfieldsGer	10.1109/ACCESS.2020.3038913 doi (DE-627)DOAJ084944153 (DE-599)DOAJ4a9efebf8e7a4ed0a366acb886bbf067 DE-627 ger DE-627 rakwb eng TK1-9971 Yujie Wu verfasserin aut Video Object Detection Guided by Object Blur Evaluation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, the excellent image-based object detection algorithms are transferred to the video object detection directly. These frame-by-frame processing methods are suboptimal owing to the degenerate object appearance such as motion blur, defocus and rare poses. The existing works for video object detection mostly focus on the feature aggregation at pixel level and instance level, but the blur impact in the aggregation process has not been exploited well so far. In this article, we propose an end-to-end blur-aid feature aggregation network (BFAN) for video object detection. The proposed BFAN focuses on the aggregation process influenced by the blur including motion blur and defocus with high accuracy and little increased computation. In BFAN, we evaluate the object blur degree of each frame as the weight for aggregation. Noteworthy, the background is usually flat which has a negative impact on the object blur degree evaluation. Therefore, we introduce a light saliency detection network to alleviate the background interference. The experiments conducted on the ImageNet VID dataset show that BFAN achieves the state-of-the-art detection performance, exactly 79.1% mAP, with 3 points improvement compared to the video object detection baseline. Video object detection object blur degree evaluation saliency detection Electrical engineering. Electronics. Nuclear engineering Hong Zhang verfasserin aut Yawei Li verfasserin aut Yifan Yang verfasserin aut Ding Yuan verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 208554-208565 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:208554-208565 https://doi.org/10.1109/ACCESS.2020.3038913 kostenfrei https://doaj.org/article/4a9efebf8e7a4ed0a366acb886bbf067 kostenfrei https://ieeexplore.ieee.org/document/9262895/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 8 2020 208554-208565
allfieldsSound	10.1109/ACCESS.2020.3038913 doi (DE-627)DOAJ084944153 (DE-599)DOAJ4a9efebf8e7a4ed0a366acb886bbf067 DE-627 ger DE-627 rakwb eng TK1-9971 Yujie Wu verfasserin aut Video Object Detection Guided by Object Blur Evaluation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, the excellent image-based object detection algorithms are transferred to the video object detection directly. These frame-by-frame processing methods are suboptimal owing to the degenerate object appearance such as motion blur, defocus and rare poses. The existing works for video object detection mostly focus on the feature aggregation at pixel level and instance level, but the blur impact in the aggregation process has not been exploited well so far. In this article, we propose an end-to-end blur-aid feature aggregation network (BFAN) for video object detection. The proposed BFAN focuses on the aggregation process influenced by the blur including motion blur and defocus with high accuracy and little increased computation. In BFAN, we evaluate the object blur degree of each frame as the weight for aggregation. Noteworthy, the background is usually flat which has a negative impact on the object blur degree evaluation. Therefore, we introduce a light saliency detection network to alleviate the background interference. The experiments conducted on the ImageNet VID dataset show that BFAN achieves the state-of-the-art detection performance, exactly 79.1% mAP, with 3 points improvement compared to the video object detection baseline. Video object detection object blur degree evaluation saliency detection Electrical engineering. Electronics. Nuclear engineering Hong Zhang verfasserin aut Yawei Li verfasserin aut Yifan Yang verfasserin aut Ding Yuan verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 208554-208565 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:208554-208565 https://doi.org/10.1109/ACCESS.2020.3038913 kostenfrei https://doaj.org/article/4a9efebf8e7a4ed0a366acb886bbf067 kostenfrei https://ieeexplore.ieee.org/document/9262895/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 8 2020 208554-208565
language	English
source	In IEEE Access 8(2020), Seite 208554-208565 volume:8 year:2020 pages:208554-208565
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authorswithroles_txt_mv	Yujie Wu @@aut@@ Hong Zhang @@aut@@ Yawei Li @@aut@@ Yifan Yang @@aut@@ Ding Yuan @@aut@@
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callnumber-first	T - Technology
author	Yujie Wu
spellingShingle	Yujie Wu misc TK1-9971 misc Video object detection misc object blur degree evaluation misc saliency detection misc Electrical engineering. Electronics. Nuclear engineering Video Object Detection Guided by Object Blur Evaluation
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topic_title	TK1-9971 Video Object Detection Guided by Object Blur Evaluation Video object detection object blur degree evaluation saliency detection
topic	misc TK1-9971 misc Video object detection misc object blur degree evaluation misc saliency detection misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Video object detection misc object blur degree evaluation misc saliency detection misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Video object detection misc object blur degree evaluation misc saliency detection misc Electrical engineering. Electronics. Nuclear engineering
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title	Video Object Detection Guided by Object Blur Evaluation
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title_full	Video Object Detection Guided by Object Blur Evaluation
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title_auth	Video Object Detection Guided by Object Blur Evaluation
abstract	In recent years, the excellent image-based object detection algorithms are transferred to the video object detection directly. These frame-by-frame processing methods are suboptimal owing to the degenerate object appearance such as motion blur, defocus and rare poses. The existing works for video object detection mostly focus on the feature aggregation at pixel level and instance level, but the blur impact in the aggregation process has not been exploited well so far. In this article, we propose an end-to-end blur-aid feature aggregation network (BFAN) for video object detection. The proposed BFAN focuses on the aggregation process influenced by the blur including motion blur and defocus with high accuracy and little increased computation. In BFAN, we evaluate the object blur degree of each frame as the weight for aggregation. Noteworthy, the background is usually flat which has a negative impact on the object blur degree evaluation. Therefore, we introduce a light saliency detection network to alleviate the background interference. The experiments conducted on the ImageNet VID dataset show that BFAN achieves the state-of-the-art detection performance, exactly 79.1% mAP, with 3 points improvement compared to the video object detection baseline.
abstractGer	In recent years, the excellent image-based object detection algorithms are transferred to the video object detection directly. These frame-by-frame processing methods are suboptimal owing to the degenerate object appearance such as motion blur, defocus and rare poses. The existing works for video object detection mostly focus on the feature aggregation at pixel level and instance level, but the blur impact in the aggregation process has not been exploited well so far. In this article, we propose an end-to-end blur-aid feature aggregation network (BFAN) for video object detection. The proposed BFAN focuses on the aggregation process influenced by the blur including motion blur and defocus with high accuracy and little increased computation. In BFAN, we evaluate the object blur degree of each frame as the weight for aggregation. Noteworthy, the background is usually flat which has a negative impact on the object blur degree evaluation. Therefore, we introduce a light saliency detection network to alleviate the background interference. The experiments conducted on the ImageNet VID dataset show that BFAN achieves the state-of-the-art detection performance, exactly 79.1% mAP, with 3 points improvement compared to the video object detection baseline.
abstract_unstemmed	In recent years, the excellent image-based object detection algorithms are transferred to the video object detection directly. These frame-by-frame processing methods are suboptimal owing to the degenerate object appearance such as motion blur, defocus and rare poses. The existing works for video object detection mostly focus on the feature aggregation at pixel level and instance level, but the blur impact in the aggregation process has not been exploited well so far. In this article, we propose an end-to-end blur-aid feature aggregation network (BFAN) for video object detection. The proposed BFAN focuses on the aggregation process influenced by the blur including motion blur and defocus with high accuracy and little increased computation. In BFAN, we evaluate the object blur degree of each frame as the weight for aggregation. Noteworthy, the background is usually flat which has a negative impact on the object blur degree evaluation. Therefore, we introduce a light saliency detection network to alleviate the background interference. The experiments conducted on the ImageNet VID dataset show that BFAN achieves the state-of-the-art detection performance, exactly 79.1% mAP, with 3 points improvement compared to the video object detection baseline.
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title_short	Video Object Detection Guided by Object Blur Evaluation
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author2	Hong Zhang Yawei Li Yifan Yang Ding Yuan
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Video Object Detection Guided by Object Blur Evaluation

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