Recognition of Abnormal Events in Surveillance Videos using Weakly Supervised Dual-Encoder Models
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915622626000896 |
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| author | Tsfaty, Noam Weizman, Avishai Cohen, Liav Tshuva, Moshe Aperstein, Yehudit |
| author_facet | Tsfaty, Noam Weizman, Avishai Cohen, Liav Tshuva, Moshe Aperstein, Yehudit |
| contents | We address the challenge of detecting rare and diverse anomalies in surveillance videos using only video-level supervision. Our dual-backbone framework combines convolutional and transformer representations through top-k pooling, achieving 90.7% area under the curve (AUC) on the UCF-Crime dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13276 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recognition of Abnormal Events in Surveillance Videos using Weakly Supervised Dual-Encoder Models Tsfaty, Noam Weizman, Avishai Cohen, Liav Tshuva, Moshe Aperstein, Yehudit Computer Vision and Pattern Recognition We address the challenge of detecting rare and diverse anomalies in surveillance videos using only video-level supervision. Our dual-backbone framework combines convolutional and transformer representations through top-k pooling, achieving 90.7% area under the curve (AUC) on the UCF-Crime dataset. |
| title | Recognition of Abnormal Events in Surveillance Videos using Weakly Supervised Dual-Encoder Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.13276 |