Publication2025
CA-VAD: Caption Aware Video Anomaly Detection in surveillance videos
The record
- Type
- Publication
- Date
- 1 September 2025
- Level
- National
- Journal
- Journal of Visual Communication and Image Representation
- Volume
- 111
- Published
- 1 September 2025
- DOI
- 10.1016/j.jvcir.2025.104521
- Scopus ID
- 105009602449
- ISSN
- 10473203
- Authors
- 5
- Open access
- No
- Citations
- 3
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
16 Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels
Video anomaly detection in surveillance improves public safety monitoring and institutional security capability.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-4.4 Public safety, cybersecurity and national security
Public safety and surveillance technology research supports security infrastructure.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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Provenance
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “CA-VAD: Caption Aware Video Anomaly Detection in surveillance videos”. Kortex.