This paper introduces a new method for detecting lens occlusion and photometric transitions in surveillance cameras. The system compares current image statistics with a clean reference, employing structured-light rejection and brightness suppression to minimize false alarms. It achieves a balanced accuracy of 0.822 and a recall of 0.925 at a 0.025 false-positive rate on various test sequences, positioning it as a reliable sensor-health subsystem. AI
IMPACT This research offers a specialized subsystem for camera integrity monitoring, potentially improving the reliability of computer vision systems in security applications.
RANK_REASON The cluster contains an academic paper published on arXiv detailing a new technical method.
- alphaXiv
- arXiv
- Bremen IoT
- CatalyzeX
- Connected Papers
- DagsHub
- Gotit.pub
- Hugging Face
- Influence Flower
- Litmaps
- ScienceCast
- scite Smart Citations
- UHCTD
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