Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities
Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manuall...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ott, S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
6 |
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| Übergeordnetes Werk: |
Enthalten in: Phagocytosis-based camera-in-situ method for pile load testing - Raza, Ali ELSEVIER, 2014transfer abstract, Amsterdam |
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| Übergeordnetes Werk: |
volume:160 ; year:2014 ; pages:132-137 ; extent:6 |
| Links: |
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| DOI / URN: |
10.1016/j.livsci.2013.12.011 |
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ELV028047605 |
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| 245 | 1 | 0 | |a Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities |
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| 520 | |a Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. | ||
| 520 | |a Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. | ||
| 650 | 7 | |a Welfare |2 Elsevier | |
| 650 | 7 | |a Video analysis |2 Elsevier | |
| 650 | 7 | |a Activity |2 Elsevier | |
| 650 | 7 | |a Behavior |2 Elsevier | |
| 650 | 7 | |a Pig |2 Elsevier | |
| 650 | 7 | |a Automated monitoring |2 Elsevier | |
| 700 | 1 | |a Moons, C.P.H. |4 oth | |
| 700 | 1 | |a Kashiha, M.A. |4 oth | |
| 700 | 1 | |a Bahr, C. |4 oth | |
| 700 | 1 | |a Tuyttens, F.A.M. |4 oth | |
| 700 | 1 | |a Berckmans, D. |4 oth | |
| 700 | 1 | |a Niewold, T.A. |4 oth | |
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10.1016/j.livsci.2013.12.011 doi GBVA2014009000012.pica (DE-627)ELV028047605 (ELSEVIER)S1871-1413(13)00556-8 DE-627 ger DE-627 rakwb eng 630 640 630 DE-600 640 DE-600 690 VZ 570 VZ BIODIV DE-30 fid 44.00 bkl Ott, S. verfasserin aut Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities 2014transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. Welfare Elsevier Video analysis Elsevier Activity Elsevier Behavior Elsevier Pig Elsevier Automated monitoring Elsevier Moons, C.P.H. oth Kashiha, M.A. oth Bahr, C. oth Tuyttens, F.A.M. oth Berckmans, D. oth Niewold, T.A. oth Enthalten in Elsevier Science Raza, Ali ELSEVIER Phagocytosis-based camera-in-situ method for pile load testing 2014transfer abstract Amsterdam (DE-627)ELV017686229 volume:160 year:2014 pages:132-137 extent:6 https://doi.org/10.1016/j.livsci.2013.12.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_70 44.00 Medizin: Allgemeines VZ AR 160 2014 132-137 6 045F 630 |
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10.1016/j.livsci.2013.12.011 doi GBVA2014009000012.pica (DE-627)ELV028047605 (ELSEVIER)S1871-1413(13)00556-8 DE-627 ger DE-627 rakwb eng 630 640 630 DE-600 640 DE-600 690 VZ 570 VZ BIODIV DE-30 fid 44.00 bkl Ott, S. verfasserin aut Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities 2014transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. Welfare Elsevier Video analysis Elsevier Activity Elsevier Behavior Elsevier Pig Elsevier Automated monitoring Elsevier Moons, C.P.H. oth Kashiha, M.A. oth Bahr, C. oth Tuyttens, F.A.M. oth Berckmans, D. oth Niewold, T.A. oth Enthalten in Elsevier Science Raza, Ali ELSEVIER Phagocytosis-based camera-in-situ method for pile load testing 2014transfer abstract Amsterdam (DE-627)ELV017686229 volume:160 year:2014 pages:132-137 extent:6 https://doi.org/10.1016/j.livsci.2013.12.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_70 44.00 Medizin: Allgemeines VZ AR 160 2014 132-137 6 045F 630 |
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10.1016/j.livsci.2013.12.011 doi GBVA2014009000012.pica (DE-627)ELV028047605 (ELSEVIER)S1871-1413(13)00556-8 DE-627 ger DE-627 rakwb eng 630 640 630 DE-600 640 DE-600 690 VZ 570 VZ BIODIV DE-30 fid 44.00 bkl Ott, S. verfasserin aut Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities 2014transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. Welfare Elsevier Video analysis Elsevier Activity Elsevier Behavior Elsevier Pig Elsevier Automated monitoring Elsevier Moons, C.P.H. oth Kashiha, M.A. oth Bahr, C. oth Tuyttens, F.A.M. oth Berckmans, D. oth Niewold, T.A. oth Enthalten in Elsevier Science Raza, Ali ELSEVIER Phagocytosis-based camera-in-situ method for pile load testing 2014transfer abstract Amsterdam (DE-627)ELV017686229 volume:160 year:2014 pages:132-137 extent:6 https://doi.org/10.1016/j.livsci.2013.12.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_70 44.00 Medizin: Allgemeines VZ AR 160 2014 132-137 6 045F 630 |
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10.1016/j.livsci.2013.12.011 doi GBVA2014009000012.pica (DE-627)ELV028047605 (ELSEVIER)S1871-1413(13)00556-8 DE-627 ger DE-627 rakwb eng 630 640 630 DE-600 640 DE-600 690 VZ 570 VZ BIODIV DE-30 fid 44.00 bkl Ott, S. verfasserin aut Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities 2014transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. Welfare Elsevier Video analysis Elsevier Activity Elsevier Behavior Elsevier Pig Elsevier Automated monitoring Elsevier Moons, C.P.H. oth Kashiha, M.A. oth Bahr, C. oth Tuyttens, F.A.M. oth Berckmans, D. oth Niewold, T.A. oth Enthalten in Elsevier Science Raza, Ali ELSEVIER Phagocytosis-based camera-in-situ method for pile load testing 2014transfer abstract Amsterdam (DE-627)ELV017686229 volume:160 year:2014 pages:132-137 extent:6 https://doi.org/10.1016/j.livsci.2013.12.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_70 44.00 Medizin: Allgemeines VZ AR 160 2014 132-137 6 045F 630 |
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10.1016/j.livsci.2013.12.011 doi GBVA2014009000012.pica (DE-627)ELV028047605 (ELSEVIER)S1871-1413(13)00556-8 DE-627 ger DE-627 rakwb eng 630 640 630 DE-600 640 DE-600 690 VZ 570 VZ BIODIV DE-30 fid 44.00 bkl Ott, S. verfasserin aut Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities 2014transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. Welfare Elsevier Video analysis Elsevier Activity Elsevier Behavior Elsevier Pig Elsevier Automated monitoring Elsevier Moons, C.P.H. oth Kashiha, M.A. oth Bahr, C. oth Tuyttens, F.A.M. oth Berckmans, D. oth Niewold, T.A. oth Enthalten in Elsevier Science Raza, Ali ELSEVIER Phagocytosis-based camera-in-situ method for pile load testing 2014transfer abstract Amsterdam (DE-627)ELV017686229 volume:160 year:2014 pages:132-137 extent:6 https://doi.org/10.1016/j.livsci.2013.12.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_70 44.00 Medizin: Allgemeines VZ AR 160 2014 132-137 6 045F 630 |
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automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities |
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Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities |
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Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. |
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Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. |
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Automated collection of continuous activity data of pigs can be performed easily using video analysis. In welfare and health research, this technique can be economically advantageous over manual observations. However, the relationship between activity measures by automated video analysis and manually scored behavioural activity has never been established. We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level. |
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We correlated automated activity measures through video analysis to ethological scores of pig activity, using off-line video recordings of four pens with grower pigs. Human observations (HO) of different behavioural activities were carried out by 2-min scan sampling during four 30-min sessions on 6 observation days. HO of pig activity was expressed as a mean proportion per session. Automated observations (AO) of pig activity were calculated by the relative number of moving pixels between two consecutive image frames (1 frame/s) and expressed as a mean image activity index per session. The overall correlation between pig activity data from AO and HO was strong and positive (R s =0.92, P<0.0001). When comparing AO and HO data at session level, the correlation coefficients for the two afternoon sessions were lower. Both static activities and activities involving locomotion had a significant effect on the activity index of AO (P<0.05), but activities that included locomotion had a three times higher effect than static activities. Further validation research is necessary, but it can be concluded that automated video analysis is a promising technique to continuously monitor behavioural activity level of pigs at pen level.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Welfare</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Video analysis</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Activity</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Behavior</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Pig</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Automated monitoring</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Moons, C.P.H.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kashiha, M.A.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bahr, C.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tuyttens, F.A.M.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Berckmans, D.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Niewold, T.A.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Raza, Ali ELSEVIER</subfield><subfield code="t">Phagocytosis-based camera-in-situ method for pile load testing</subfield><subfield code="d">2014transfer abstract</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV017686229</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:160</subfield><subfield code="g">year:2014</subfield><subfield code="g">pages:132-137</subfield><subfield code="g">extent:6</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.livsci.2013.12.011</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.00</subfield><subfield code="j">Medizin: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">160</subfield><subfield code="j">2014</subfield><subfield code="h">132-137</subfield><subfield code="g">6</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">630</subfield></datafield></record></collection>
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