Enhancing Weakly-Supervised Video Anomaly Detection With Temporal Constraints
Anomaly Detection has been a significant field in Machine Learning since it began gaining traction. In the context of Computer Vision, the increased interest is notorious as it enables the development of video processing models for different tasks without the need for a cumbersome effort with the an...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10965669/ |
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| author | Francisco Caetano Pedro Carvalho Christina Mastralexi Jaime S. Cardoso |
| author_facet | Francisco Caetano Pedro Carvalho Christina Mastralexi Jaime S. Cardoso |
| author_sort | Francisco Caetano |
| collection | DOAJ |
| description | Anomaly Detection has been a significant field in Machine Learning since it began gaining traction. In the context of Computer Vision, the increased interest is notorious as it enables the development of video processing models for different tasks without the need for a cumbersome effort with the annotation of possible events, that may be under represented. From the predominant strategies, weakly and semi-supervised, the former has demonstrated potential to achieve a higher score in its analysis, adding to its flexibility. This work shows that using temporal ranking constraints for Multiple Instance Learning can increase the performance of these models, allowing the focus on the most informative instances. Moreover, the results suggest that altering the ranking process to include information about adjacent instances generates best-performing models. |
| format | Article |
| id | doaj-art-a8fcc806521e481086bfbbbdc2aaa5d9 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a8fcc806521e481086bfbbbdc2aaa5d92025-08-20T02:20:23ZengIEEEIEEE Access2169-35362025-01-0113708827089410.1109/ACCESS.2025.356076710965669Enhancing Weakly-Supervised Video Anomaly Detection With Temporal ConstraintsFrancisco Caetano0Pedro Carvalho1https://orcid.org/0000-0003-4983-4316Christina Mastralexi2https://orcid.org/0009-0002-0992-8801Jaime S. Cardoso3https://orcid.org/0000-0002-3760-2473INESC TEC—Institute for Systems and Computer Engineering, Technology, and Science, Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology, and Science, Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology, and Science, Porto, PortugalINESC TEC—Institute for Systems and Computer Engineering, Technology, and Science, Porto, PortugalAnomaly Detection has been a significant field in Machine Learning since it began gaining traction. In the context of Computer Vision, the increased interest is notorious as it enables the development of video processing models for different tasks without the need for a cumbersome effort with the annotation of possible events, that may be under represented. From the predominant strategies, weakly and semi-supervised, the former has demonstrated potential to achieve a higher score in its analysis, adding to its flexibility. This work shows that using temporal ranking constraints for Multiple Instance Learning can increase the performance of these models, allowing the focus on the most informative instances. Moreover, the results suggest that altering the ranking process to include information about adjacent instances generates best-performing models.https://ieeexplore.ieee.org/document/10965669/Multiple instance learningtemporal constraintsvideo anomaly detectionweakly-supervised models |
| spellingShingle | Francisco Caetano Pedro Carvalho Christina Mastralexi Jaime S. Cardoso Enhancing Weakly-Supervised Video Anomaly Detection With Temporal Constraints IEEE Access Multiple instance learning temporal constraints video anomaly detection weakly-supervised models |
| title | Enhancing Weakly-Supervised Video Anomaly Detection With Temporal Constraints |
| title_full | Enhancing Weakly-Supervised Video Anomaly Detection With Temporal Constraints |
| title_fullStr | Enhancing Weakly-Supervised Video Anomaly Detection With Temporal Constraints |
| title_full_unstemmed | Enhancing Weakly-Supervised Video Anomaly Detection With Temporal Constraints |
| title_short | Enhancing Weakly-Supervised Video Anomaly Detection With Temporal Constraints |
| title_sort | enhancing weakly supervised video anomaly detection with temporal constraints |
| topic | Multiple instance learning temporal constraints video anomaly detection weakly-supervised models |
| url | https://ieeexplore.ieee.org/document/10965669/ |
| work_keys_str_mv | AT franciscocaetano enhancingweaklysupervisedvideoanomalydetectionwithtemporalconstraints AT pedrocarvalho enhancingweaklysupervisedvideoanomalydetectionwithtemporalconstraints AT christinamastralexi enhancingweaklysupervisedvideoanomalydetectionwithtemporalconstraints AT jaimescardoso enhancingweaklysupervisedvideoanomalydetectionwithtemporalconstraints |