Automated camera sabotage detection for enhancing video surveillance systems
Abstract Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sitara, K. [verfasserIn] Mehtre, B. M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 78(2018), 5 vom: 01. Juni, Seite 5819-5841 |
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| Übergeordnetes Werk: |
volume:78 ; year:2018 ; number:5 ; day:01 ; month:06 ; pages:5819-5841 |
| Links: |
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| DOI / URN: |
10.1007/s11042-018-6165-4 |
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| Katalog-ID: |
SPR016034643 |
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| 520 | |a Abstract Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the physical device itself, in order to conceal their dubious activities. Generally, surveillance systems are unmanned due to limitations of manual monitoring. Automatic detection of camera tamper events is crucial for timely operator intervention. We propose a new method for detecting video camera tampering events like occlusion, defocus and displacement. The features used are edge information, frame count, foreground objects’ coverage area and its static nature. Effectiveness of our method is tested through experimentation on public datasets. The results obtained are encouraging with high detection and low false alarm rates. The proposed method automatically detects routine problems with cameras like dirt on camera lens, fog and smoke. | ||
| 650 | 4 | |a Camera tampering |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Camera sabotage |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Video surveillance |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Camera occlusion |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Camera defocus |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Camera displacement |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Secure systems |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Video forensics |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Mehtre, B. M. |e verfasserin |4 aut | |
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10.1007/s11042-018-6165-4 doi (DE-627)SPR016034643 (SPR)s11042-018-6165-4-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Sitara, K. verfasserin aut Automated camera sabotage detection for enhancing video surveillance systems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the physical device itself, in order to conceal their dubious activities. Generally, surveillance systems are unmanned due to limitations of manual monitoring. Automatic detection of camera tamper events is crucial for timely operator intervention. We propose a new method for detecting video camera tampering events like occlusion, defocus and displacement. The features used are edge information, frame count, foreground objects’ coverage area and its static nature. Effectiveness of our method is tested through experimentation on public datasets. The results obtained are encouraging with high detection and low false alarm rates. The proposed method automatically detects routine problems with cameras like dirt on camera lens, fog and smoke. Camera tampering (dpeaa)DE-He213 Camera sabotage (dpeaa)DE-He213 Video surveillance (dpeaa)DE-He213 Camera occlusion (dpeaa)DE-He213 Camera defocus (dpeaa)DE-He213 Camera displacement (dpeaa)DE-He213 Secure systems (dpeaa)DE-He213 Video forensics (dpeaa)DE-He213 Mehtre, B. M. verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 78(2018), 5 vom: 01. Juni, Seite 5819-5841 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:78 year:2018 number:5 day:01 month:06 pages:5819-5841 https://dx.doi.org/10.1007/s11042-018-6165-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4393 GBV_ILN_4700 54.87 ASE AR 78 2018 5 01 06 5819-5841 |
| spelling |
10.1007/s11042-018-6165-4 doi (DE-627)SPR016034643 (SPR)s11042-018-6165-4-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Sitara, K. verfasserin aut Automated camera sabotage detection for enhancing video surveillance systems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the physical device itself, in order to conceal their dubious activities. Generally, surveillance systems are unmanned due to limitations of manual monitoring. Automatic detection of camera tamper events is crucial for timely operator intervention. We propose a new method for detecting video camera tampering events like occlusion, defocus and displacement. The features used are edge information, frame count, foreground objects’ coverage area and its static nature. Effectiveness of our method is tested through experimentation on public datasets. The results obtained are encouraging with high detection and low false alarm rates. The proposed method automatically detects routine problems with cameras like dirt on camera lens, fog and smoke. Camera tampering (dpeaa)DE-He213 Camera sabotage (dpeaa)DE-He213 Video surveillance (dpeaa)DE-He213 Camera occlusion (dpeaa)DE-He213 Camera defocus (dpeaa)DE-He213 Camera displacement (dpeaa)DE-He213 Secure systems (dpeaa)DE-He213 Video forensics (dpeaa)DE-He213 Mehtre, B. M. verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 78(2018), 5 vom: 01. Juni, Seite 5819-5841 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:78 year:2018 number:5 day:01 month:06 pages:5819-5841 https://dx.doi.org/10.1007/s11042-018-6165-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4393 GBV_ILN_4700 54.87 ASE AR 78 2018 5 01 06 5819-5841 |
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10.1007/s11042-018-6165-4 doi (DE-627)SPR016034643 (SPR)s11042-018-6165-4-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Sitara, K. verfasserin aut Automated camera sabotage detection for enhancing video surveillance systems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the physical device itself, in order to conceal their dubious activities. Generally, surveillance systems are unmanned due to limitations of manual monitoring. Automatic detection of camera tamper events is crucial for timely operator intervention. We propose a new method for detecting video camera tampering events like occlusion, defocus and displacement. The features used are edge information, frame count, foreground objects’ coverage area and its static nature. Effectiveness of our method is tested through experimentation on public datasets. The results obtained are encouraging with high detection and low false alarm rates. The proposed method automatically detects routine problems with cameras like dirt on camera lens, fog and smoke. Camera tampering (dpeaa)DE-He213 Camera sabotage (dpeaa)DE-He213 Video surveillance (dpeaa)DE-He213 Camera occlusion (dpeaa)DE-He213 Camera defocus (dpeaa)DE-He213 Camera displacement (dpeaa)DE-He213 Secure systems (dpeaa)DE-He213 Video forensics (dpeaa)DE-He213 Mehtre, B. M. verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 78(2018), 5 vom: 01. Juni, Seite 5819-5841 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:78 year:2018 number:5 day:01 month:06 pages:5819-5841 https://dx.doi.org/10.1007/s11042-018-6165-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4393 GBV_ILN_4700 54.87 ASE AR 78 2018 5 01 06 5819-5841 |
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10.1007/s11042-018-6165-4 doi (DE-627)SPR016034643 (SPR)s11042-018-6165-4-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Sitara, K. verfasserin aut Automated camera sabotage detection for enhancing video surveillance systems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the physical device itself, in order to conceal their dubious activities. Generally, surveillance systems are unmanned due to limitations of manual monitoring. Automatic detection of camera tamper events is crucial for timely operator intervention. We propose a new method for detecting video camera tampering events like occlusion, defocus and displacement. The features used are edge information, frame count, foreground objects’ coverage area and its static nature. Effectiveness of our method is tested through experimentation on public datasets. The results obtained are encouraging with high detection and low false alarm rates. The proposed method automatically detects routine problems with cameras like dirt on camera lens, fog and smoke. Camera tampering (dpeaa)DE-He213 Camera sabotage (dpeaa)DE-He213 Video surveillance (dpeaa)DE-He213 Camera occlusion (dpeaa)DE-He213 Camera defocus (dpeaa)DE-He213 Camera displacement (dpeaa)DE-He213 Secure systems (dpeaa)DE-He213 Video forensics (dpeaa)DE-He213 Mehtre, B. M. verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 78(2018), 5 vom: 01. Juni, Seite 5819-5841 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:78 year:2018 number:5 day:01 month:06 pages:5819-5841 https://dx.doi.org/10.1007/s11042-018-6165-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4393 GBV_ILN_4700 54.87 ASE AR 78 2018 5 01 06 5819-5841 |
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10.1007/s11042-018-6165-4 doi (DE-627)SPR016034643 (SPR)s11042-018-6165-4-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Sitara, K. verfasserin aut Automated camera sabotage detection for enhancing video surveillance systems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the physical device itself, in order to conceal their dubious activities. Generally, surveillance systems are unmanned due to limitations of manual monitoring. Automatic detection of camera tamper events is crucial for timely operator intervention. We propose a new method for detecting video camera tampering events like occlusion, defocus and displacement. The features used are edge information, frame count, foreground objects’ coverage area and its static nature. Effectiveness of our method is tested through experimentation on public datasets. The results obtained are encouraging with high detection and low false alarm rates. The proposed method automatically detects routine problems with cameras like dirt on camera lens, fog and smoke. Camera tampering (dpeaa)DE-He213 Camera sabotage (dpeaa)DE-He213 Video surveillance (dpeaa)DE-He213 Camera occlusion (dpeaa)DE-He213 Camera defocus (dpeaa)DE-He213 Camera displacement (dpeaa)DE-He213 Secure systems (dpeaa)DE-He213 Video forensics (dpeaa)DE-He213 Mehtre, B. M. verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 78(2018), 5 vom: 01. Juni, Seite 5819-5841 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:78 year:2018 number:5 day:01 month:06 pages:5819-5841 https://dx.doi.org/10.1007/s11042-018-6165-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4393 GBV_ILN_4700 54.87 ASE AR 78 2018 5 01 06 5819-5841 |
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Sitara, K. |
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Sitara, K. ddc 070 bkl 54.87 misc Camera tampering misc Camera sabotage misc Video surveillance misc Camera occlusion misc Camera defocus misc Camera displacement misc Secure systems misc Video forensics Automated camera sabotage detection for enhancing video surveillance systems |
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070 004 ASE 54.87 bkl Automated camera sabotage detection for enhancing video surveillance systems Camera tampering (dpeaa)DE-He213 Camera sabotage (dpeaa)DE-He213 Video surveillance (dpeaa)DE-He213 Camera occlusion (dpeaa)DE-He213 Camera defocus (dpeaa)DE-He213 Camera displacement (dpeaa)DE-He213 Secure systems (dpeaa)DE-He213 Video forensics (dpeaa)DE-He213 |
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automated camera sabotage detection for enhancing video surveillance systems |
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Automated camera sabotage detection for enhancing video surveillance systems |
| abstract |
Abstract Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the physical device itself, in order to conceal their dubious activities. Generally, surveillance systems are unmanned due to limitations of manual monitoring. Automatic detection of camera tamper events is crucial for timely operator intervention. We propose a new method for detecting video camera tampering events like occlusion, defocus and displacement. The features used are edge information, frame count, foreground objects’ coverage area and its static nature. Effectiveness of our method is tested through experimentation on public datasets. The results obtained are encouraging with high detection and low false alarm rates. The proposed method automatically detects routine problems with cameras like dirt on camera lens, fog and smoke. |
| abstractGer |
Abstract Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the physical device itself, in order to conceal their dubious activities. Generally, surveillance systems are unmanned due to limitations of manual monitoring. Automatic detection of camera tamper events is crucial for timely operator intervention. We propose a new method for detecting video camera tampering events like occlusion, defocus and displacement. The features used are edge information, frame count, foreground objects’ coverage area and its static nature. Effectiveness of our method is tested through experimentation on public datasets. The results obtained are encouraging with high detection and low false alarm rates. The proposed method automatically detects routine problems with cameras like dirt on camera lens, fog and smoke. |
| abstract_unstemmed |
Abstract Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the physical device itself, in order to conceal their dubious activities. Generally, surveillance systems are unmanned due to limitations of manual monitoring. Automatic detection of camera tamper events is crucial for timely operator intervention. We propose a new method for detecting video camera tampering events like occlusion, defocus and displacement. The features used are edge information, frame count, foreground objects’ coverage area and its static nature. Effectiveness of our method is tested through experimentation on public datasets. The results obtained are encouraging with high detection and low false alarm rates. The proposed method automatically detects routine problems with cameras like dirt on camera lens, fog and smoke. |
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5 |
| title_short |
Automated camera sabotage detection for enhancing video surveillance systems |
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https://dx.doi.org/10.1007/s11042-018-6165-4 |
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true |
| author2 |
Mehtre, B. M. |
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Mehtre, B. M. |
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| doi_str |
10.1007/s11042-018-6165-4 |
| up_date |
2024-07-03T20:19:43.091Z |
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1803590548162871296 |
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| score |
7.4005537 |