通过复用I帧推理，高效检测压缩视频中的隐私目标。
ComPrivDet: Efficient Privacy Object Detection in Compressed Domains Through Inference Reuse
- 复用I帧推理结果，动态决定是否跳过或轻量修正P/B帧检测。
- 隐私人脸检测准确率99.75%，车牌检测96.83%，跳过超80%推理。
- 相比现有方法，准确率高9.84%，延迟降低75.95%，适合边缘设备部署。
随着物联网深入日常生活，用户对视频数据泄露的隐私担忧日益增加。在大规模视频分析（如智慧社区）中，逐帧保护引入显著延迟，因此更优策略是仅对含隐私目标（如人脸）的帧进行保护。现有目标检测器需完全解码视频或对压缩视频逐帧处理，导致解码开销或精度下降。为此，我们提出ComPrivDet，一种在压缩域高效检测隐私目标的方法，通过复用I帧的推理结果实现。利用压缩域特征判断新目标是否存在，ComPrivDet可跳过部分P/B帧检测，或以轻量级检测器高效修正。该方法在隐私人脸检测中保持99.75%准确率，在隐私车牌检测中达96.83%，同时跳过超过80%的推理计算。相较现有压缩域检测方法，平均准确率提升9.84%，延迟降低75.95%。
原文摘要 · Abstract (English)
As the Internet of Things (IoT) becomes deeply embedded in daily life, users are increasingly concerned about privacy leakage, especially from video data. Since frame-by-frame protection in large-scale video analytics (e.g., smart communities) introduces significant latency, a more efficient solution is to selectively protect frames containing privacy objects (e.g., faces). Existing object detectors require fully decoded videos or per-frame processing in compressed videos, leading to decoding overhead or reduced accuracy. Therefore, we propose ComPrivDet, an efficient method for detecting privacy objects in compressed video by reusing I-frame inference results. By identifying the presence of new objects through compressed-domain cues, ComPrivDet either skips P- and B-frame detections or efficiently refines them with a lightweight detector. ComPrivDet maintains 99.75% accuracy in private face detection and 96.83% in private license plate detection while skipping over 80% of inferences. It averages 9.84% higher accuracy with 75.95% lower latency than existing compressed-domain detection methods.
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