Leveraging orientation for weakly supervised object detection with application to firearm localization

Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and...
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Gespeichert in:

Autor*in:	Iqbal, Javed [verfasserIn] Munir, Muhammad Akhtar [verfasserIn] Mahmood, Arif [verfasserIn] Ali, Afsheen Rafaqat [verfasserIn] Ali, Mohsen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Oriented object detection Firearms detection Gun violence Surveillance and security Weakly-supervised object detection Deep convolutional neural networks

Übergeordnetes Werk:	Enthalten in: Neurocomputing - Amsterdam : Elsevier, 1989, 440, Seite 310-320
Übergeordnetes Werk:	volume:440 ; pages:310-320

DOI / URN:	10.1016/j.neucom.2021.01.075

Katalog-ID:	ELV005872243

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245	1	0	\|a Leveraging orientation for weakly supervised object detection with application to firearm localization
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520			\|a Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented object bounding boxes (OBB) while using Axis-Aligned Bounding Boxes (AABB) for training. The proposed OAOD is different from the existing oriented object detectors which strictly require OBB during training which may not always be present. The goal of training on AABB and detection of OBB is achieved by employing a multistage scheme, with Stage-1 predicting the AABB and Stage-2 predicting OBB. In-between the two stages, the oriented proposal generation module along with the object aligned RoI pooling is designed to extract features based on the predicted orientation and to make these features orientation invariant. A diverse and challenging dataset consisting of eleven thousand images is also proposed for firearm detection which is manually annotated for firearm classification and localization. The proposed ITU Firearm dataset (ITUF) contains a wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors. OAOD has outperformed both types of object detectors with a significant margin. The experimental results (mAP: 88.3 on AABB & mAP: 77.5 on OBB) demonstrate effectiveness of the proposed algorithm for firearm detection.
650		4	\|a Oriented object detection
650		4	\|a Firearms detection
650		4	\|a Gun violence
650		4	\|a Surveillance and security
650		4	\|a Weakly-supervised object detection
650		4	\|a Deep convolutional neural networks
700	1		\|a Munir, Muhammad Akhtar \|e verfasserin \|4 aut
700	1		\|a Mahmood, Arif \|e verfasserin \|4 aut
700	1		\|a Ali, Afsheen Rafaqat \|e verfasserin \|4 aut
700	1		\|a Ali, Mohsen \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Neurocomputing \|d Amsterdam : Elsevier, 1989 \|g 440, Seite 310-320 \|h Online-Ressource \|w (DE-627)271176008 \|w (DE-600)1479006-3 \|w (DE-576)078412358 \|x 1872-8286 \|7 nnns
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912			\|a GBV_ILN_2004
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912			\|a GBV_ILN_2038
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912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
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912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 54.72 \|j Künstliche Intelligenz
951			\|a AR
952			\|d 440 \|h 310-320

Indexfelder

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matchkey_str	article:18728286:2021----::eeaigrettofrekyuevsdbeteetowtapia
hierarchy_sort_str	2021
bklnumber	54.72
publishDate	2021
allfields	10.1016/j.neucom.2021.01.075 doi (DE-627)ELV005872243 (ELSEVIER)S0925-2312(21)00151-X DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Iqbal, Javed verfasserin aut Leveraging orientation for weakly supervised object detection with application to firearm localization 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented object bounding boxes (OBB) while using Axis-Aligned Bounding Boxes (AABB) for training. The proposed OAOD is different from the existing oriented object detectors which strictly require OBB during training which may not always be present. The goal of training on AABB and detection of OBB is achieved by employing a multistage scheme, with Stage-1 predicting the AABB and Stage-2 predicting OBB. In-between the two stages, the oriented proposal generation module along with the object aligned RoI pooling is designed to extract features based on the predicted orientation and to make these features orientation invariant. A diverse and challenging dataset consisting of eleven thousand images is also proposed for firearm detection which is manually annotated for firearm classification and localization. The proposed ITU Firearm dataset (ITUF) contains a wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors. OAOD has outperformed both types of object detectors with a significant margin. The experimental results (mAP: 88.3 on AABB & mAP: 77.5 on OBB) demonstrate effectiveness of the proposed algorithm for firearm detection. Oriented object detection Firearms detection Gun violence Surveillance and security Weakly-supervised object detection Deep convolutional neural networks Munir, Muhammad Akhtar verfasserin aut Mahmood, Arif verfasserin aut Ali, Afsheen Rafaqat verfasserin aut Ali, Mohsen verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 440, Seite 310-320 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:440 pages:310-320 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 440 310-320
spelling	10.1016/j.neucom.2021.01.075 doi (DE-627)ELV005872243 (ELSEVIER)S0925-2312(21)00151-X DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Iqbal, Javed verfasserin aut Leveraging orientation for weakly supervised object detection with application to firearm localization 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented object bounding boxes (OBB) while using Axis-Aligned Bounding Boxes (AABB) for training. The proposed OAOD is different from the existing oriented object detectors which strictly require OBB during training which may not always be present. The goal of training on AABB and detection of OBB is achieved by employing a multistage scheme, with Stage-1 predicting the AABB and Stage-2 predicting OBB. In-between the two stages, the oriented proposal generation module along with the object aligned RoI pooling is designed to extract features based on the predicted orientation and to make these features orientation invariant. A diverse and challenging dataset consisting of eleven thousand images is also proposed for firearm detection which is manually annotated for firearm classification and localization. The proposed ITU Firearm dataset (ITUF) contains a wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors. OAOD has outperformed both types of object detectors with a significant margin. The experimental results (mAP: 88.3 on AABB & mAP: 77.5 on OBB) demonstrate effectiveness of the proposed algorithm for firearm detection. Oriented object detection Firearms detection Gun violence Surveillance and security Weakly-supervised object detection Deep convolutional neural networks Munir, Muhammad Akhtar verfasserin aut Mahmood, Arif verfasserin aut Ali, Afsheen Rafaqat verfasserin aut Ali, Mohsen verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 440, Seite 310-320 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:440 pages:310-320 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 440 310-320
allfields_unstemmed	10.1016/j.neucom.2021.01.075 doi (DE-627)ELV005872243 (ELSEVIER)S0925-2312(21)00151-X DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Iqbal, Javed verfasserin aut Leveraging orientation for weakly supervised object detection with application to firearm localization 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented object bounding boxes (OBB) while using Axis-Aligned Bounding Boxes (AABB) for training. The proposed OAOD is different from the existing oriented object detectors which strictly require OBB during training which may not always be present. The goal of training on AABB and detection of OBB is achieved by employing a multistage scheme, with Stage-1 predicting the AABB and Stage-2 predicting OBB. In-between the two stages, the oriented proposal generation module along with the object aligned RoI pooling is designed to extract features based on the predicted orientation and to make these features orientation invariant. A diverse and challenging dataset consisting of eleven thousand images is also proposed for firearm detection which is manually annotated for firearm classification and localization. The proposed ITU Firearm dataset (ITUF) contains a wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors. OAOD has outperformed both types of object detectors with a significant margin. The experimental results (mAP: 88.3 on AABB & mAP: 77.5 on OBB) demonstrate effectiveness of the proposed algorithm for firearm detection. Oriented object detection Firearms detection Gun violence Surveillance and security Weakly-supervised object detection Deep convolutional neural networks Munir, Muhammad Akhtar verfasserin aut Mahmood, Arif verfasserin aut Ali, Afsheen Rafaqat verfasserin aut Ali, Mohsen verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 440, Seite 310-320 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:440 pages:310-320 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 440 310-320
allfieldsGer	10.1016/j.neucom.2021.01.075 doi (DE-627)ELV005872243 (ELSEVIER)S0925-2312(21)00151-X DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Iqbal, Javed verfasserin aut Leveraging orientation for weakly supervised object detection with application to firearm localization 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented object bounding boxes (OBB) while using Axis-Aligned Bounding Boxes (AABB) for training. The proposed OAOD is different from the existing oriented object detectors which strictly require OBB during training which may not always be present. The goal of training on AABB and detection of OBB is achieved by employing a multistage scheme, with Stage-1 predicting the AABB and Stage-2 predicting OBB. In-between the two stages, the oriented proposal generation module along with the object aligned RoI pooling is designed to extract features based on the predicted orientation and to make these features orientation invariant. A diverse and challenging dataset consisting of eleven thousand images is also proposed for firearm detection which is manually annotated for firearm classification and localization. The proposed ITU Firearm dataset (ITUF) contains a wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors. OAOD has outperformed both types of object detectors with a significant margin. The experimental results (mAP: 88.3 on AABB & mAP: 77.5 on OBB) demonstrate effectiveness of the proposed algorithm for firearm detection. Oriented object detection Firearms detection Gun violence Surveillance and security Weakly-supervised object detection Deep convolutional neural networks Munir, Muhammad Akhtar verfasserin aut Mahmood, Arif verfasserin aut Ali, Afsheen Rafaqat verfasserin aut Ali, Mohsen verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 440, Seite 310-320 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:440 pages:310-320 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 440 310-320
allfieldsSound	10.1016/j.neucom.2021.01.075 doi (DE-627)ELV005872243 (ELSEVIER)S0925-2312(21)00151-X DE-627 ger DE-627 rda eng 610 DE-600 54.72 bkl Iqbal, Javed verfasserin aut Leveraging orientation for weakly supervised object detection with application to firearm localization 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented object bounding boxes (OBB) while using Axis-Aligned Bounding Boxes (AABB) for training. The proposed OAOD is different from the existing oriented object detectors which strictly require OBB during training which may not always be present. The goal of training on AABB and detection of OBB is achieved by employing a multistage scheme, with Stage-1 predicting the AABB and Stage-2 predicting OBB. In-between the two stages, the oriented proposal generation module along with the object aligned RoI pooling is designed to extract features based on the predicted orientation and to make these features orientation invariant. A diverse and challenging dataset consisting of eleven thousand images is also proposed for firearm detection which is manually annotated for firearm classification and localization. The proposed ITU Firearm dataset (ITUF) contains a wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors. OAOD has outperformed both types of object detectors with a significant margin. The experimental results (mAP: 88.3 on AABB & mAP: 77.5 on OBB) demonstrate effectiveness of the proposed algorithm for firearm detection. Oriented object detection Firearms detection Gun violence Surveillance and security Weakly-supervised object detection Deep convolutional neural networks Munir, Muhammad Akhtar verfasserin aut Mahmood, Arif verfasserin aut Ali, Afsheen Rafaqat verfasserin aut Ali, Mohsen verfasserin aut Enthalten in Neurocomputing Amsterdam : Elsevier, 1989 440, Seite 310-320 Online-Ressource (DE-627)271176008 (DE-600)1479006-3 (DE-576)078412358 1872-8286 nnns volume:440 pages:310-320 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 440 310-320
language	English
source	Enthalten in Neurocomputing 440, Seite 310-320 volume:440 pages:310-320
sourceStr	Enthalten in Neurocomputing 440, Seite 310-320 volume:440 pages:310-320
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Oriented object detection Firearms detection Gun violence Surveillance and security Weakly-supervised object detection Deep convolutional neural networks
dewey-raw	610
isfreeaccess_bool	false
container_title	Neurocomputing
authorswithroles_txt_mv	Iqbal, Javed @@aut@@ Munir, Muhammad Akhtar @@aut@@ Mahmood, Arif @@aut@@ Ali, Afsheen Rafaqat @@aut@@ Ali, Mohsen @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	271176008
dewey-sort	3610
id	ELV005872243
language_de	englisch
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author	Iqbal, Javed
spellingShingle	Iqbal, Javed ddc 610 bkl 54.72 misc Oriented object detection misc Firearms detection misc Gun violence misc Surveillance and security misc Weakly-supervised object detection misc Deep convolutional neural networks Leveraging orientation for weakly supervised object detection with application to firearm localization
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topic_title	610 DE-600 54.72 bkl Leveraging orientation for weakly supervised object detection with application to firearm localization Oriented object detection Firearms detection Gun violence Surveillance and security Weakly-supervised object detection Deep convolutional neural networks
topic	ddc 610 bkl 54.72 misc Oriented object detection misc Firearms detection misc Gun violence misc Surveillance and security misc Weakly-supervised object detection misc Deep convolutional neural networks
topic_unstemmed	ddc 610 bkl 54.72 misc Oriented object detection misc Firearms detection misc Gun violence misc Surveillance and security misc Weakly-supervised object detection misc Deep convolutional neural networks
topic_browse	ddc 610 bkl 54.72 misc Oriented object detection misc Firearms detection misc Gun violence misc Surveillance and security misc Weakly-supervised object detection misc Deep convolutional neural networks
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title	Leveraging orientation for weakly supervised object detection with application to firearm localization
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title_full	Leveraging orientation for weakly supervised object detection with application to firearm localization
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title_sort	leveraging orientation for weakly supervised object detection with application to firearm localization
title_auth	Leveraging orientation for weakly supervised object detection with application to firearm localization
abstract	Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented object bounding boxes (OBB) while using Axis-Aligned Bounding Boxes (AABB) for training. The proposed OAOD is different from the existing oriented object detectors which strictly require OBB during training which may not always be present. The goal of training on AABB and detection of OBB is achieved by employing a multistage scheme, with Stage-1 predicting the AABB and Stage-2 predicting OBB. In-between the two stages, the oriented proposal generation module along with the object aligned RoI pooling is designed to extract features based on the predicted orientation and to make these features orientation invariant. A diverse and challenging dataset consisting of eleven thousand images is also proposed for firearm detection which is manually annotated for firearm classification and localization. The proposed ITU Firearm dataset (ITUF) contains a wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors. OAOD has outperformed both types of object detectors with a significant margin. The experimental results (mAP: 88.3 on AABB & mAP: 77.5 on OBB) demonstrate effectiveness of the proposed algorithm for firearm detection.
abstractGer	Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented object bounding boxes (OBB) while using Axis-Aligned Bounding Boxes (AABB) for training. The proposed OAOD is different from the existing oriented object detectors which strictly require OBB during training which may not always be present. The goal of training on AABB and detection of OBB is achieved by employing a multistage scheme, with Stage-1 predicting the AABB and Stage-2 predicting OBB. In-between the two stages, the oriented proposal generation module along with the object aligned RoI pooling is designed to extract features based on the predicted orientation and to make these features orientation invariant. A diverse and challenging dataset consisting of eleven thousand images is also proposed for firearm detection which is manually annotated for firearm classification and localization. The proposed ITU Firearm dataset (ITUF) contains a wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors. OAOD has outperformed both types of object detectors with a significant margin. The experimental results (mAP: 88.3 on AABB & mAP: 77.5 on OBB) demonstrate effectiveness of the proposed algorithm for firearm detection.
abstract_unstemmed	Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented object bounding boxes (OBB) while using Axis-Aligned Bounding Boxes (AABB) for training. The proposed OAOD is different from the existing oriented object detectors which strictly require OBB during training which may not always be present. The goal of training on AABB and detection of OBB is achieved by employing a multistage scheme, with Stage-1 predicting the AABB and Stage-2 predicting OBB. In-between the two stages, the oriented proposal generation module along with the object aligned RoI pooling is designed to extract features based on the predicted orientation and to make these features orientation invariant. A diverse and challenging dataset consisting of eleven thousand images is also proposed for firearm detection which is manually annotated for firearm classification and localization. The proposed ITU Firearm dataset (ITUF) contains a wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors. OAOD has outperformed both types of object detectors with a significant margin. The experimental results (mAP: 88.3 on AABB & mAP: 77.5 on OBB) demonstrate effectiveness of the proposed algorithm for firearm detection.
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title_short	Leveraging orientation for weakly supervised object detection with application to firearm localization
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author2	Munir, Muhammad Akhtar Mahmood, Arif Ali, Afsheen Rafaqat Ali, Mohsen
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up_date	2024-07-06T19:27:10.953Z
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Leveraging orientation for weakly supervised object detection with application to firearm localization

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