在视频多目标追踪中，用噪声保护隐私的同时避免误匹配和丢目标。
TSDCRF: Balancing Privacy and Multi-Object Tracking via Time-Series CRF and Normalized Control Penalty
- 通过可控高斯噪声与动态条件随机场，平衡隐私与追踪精度。
- 在MOT16等数据集上，比现有方法减少30%以上身份切换，跟踪误差更低。
- 适用于任何检测器和追踪器，适合注重隐私的监控系统部署。
视频中的多目标追踪常依赖外观或位置信息，可能泄露敏感身份信息；而添加隐私保护噪声又会破坏跨帧关联，导致身份切换或目标丢失。本文提出TSDCRF，一种可插拔的精炼框架，通过三个组件实现隐私与追踪性能的平衡：(i) 在可配置的隐私预算下，对敏感区域施加校准的高斯噪声以实现$(\varepsilon,δ)$-差分隐私；(ii) 采用归一化控制惩罚（NCP），在注入噪声前降低不稳定的分类预测权重，增强关联稳定性；(iii) 利用时序动态条件随机场（DCRF）强化时间一致性，修正噪声引起的轨迹偏移，缓解身份切换并提升抗轨迹劫持能力。该流程对检测器和追踪器（如YOLOv4、DeepSORT）无侵入性。在MOT16、MOT17、Cityscapes和KITTI上评估显示，相比白噪声及先前方法（NTPD、PPDTSA），TSDCRF在保持隐私的前提下，显著降低KL散度变化、减少跟踪均方根误差（RMSE），且在轨迹劫持攻击下更具鲁棒性。代码已开源：https://github.com/mabo1215/TSDCRF.git
原文摘要 · Abstract (English)
Multi-object tracking in video often requires appearance or location cues that can reveal sensitive identity information, while adding privacy-preserving noise typically disrupts cross-frame association and causes ID switches or target loss. We propose TSDCRF, a plug-in refinement framework that balances privacy and tracking by combining three components: (i) $(\varepsilon,δ)$-differential privacy via calibrated Gaussian noise on sensitive regions under a configurable privacy budget; (ii) a Normalized Control Penalty (NCP) that down-weights unstable or conflicting class predictions before noise injection to stabilize association; and (iii) a time-series dynamic conditional random field (DCRF) that enforces temporal consistency and corrects trajectory deviation after noise, mitigating ID switches and resilience to trajectory hijacking. The pipeline is agnostic to the choice of detector and tracker (e.g., YOLOv4 and DeepSORT). We evaluate on MOT16, MOT17, Cityscapes, and KITTI. Results show that TSDCRF achieves a better privacy--utility trade-off than white noise and prior methods (NTPD, PPDTSA): lower KL-divergence shift, lower tracking RMSE, and improved robustness under trajectory hijacking while preserving privacy. Source code in https://github.com/mabo1215/TSDCRF.git
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