摘要：尽管深度学习在视频异常检测（VAD）领域展现出良好的发展前景，但此类模型的可解释性（XAI）仍是一项严峻挑战。兼具可解释性与隐私保护能力的 VAD 系统有助于建立透明度与信任，从而推动计算机视觉在安全与安防监控等领域的落地应用。然而，隐私保护型 VAD 系统中的异常可解释性研究目前仍相对不足。在本研究中，我们提出了 XAVAD 方法——这是一种面向匿名化 VAD 的统合性描述型异常 XAI 框架，集成了灵活的零样本匿名化、异常类型归类、视觉空间定位以及场景上下文描述功能。我们对集成了全身视觉匿名化（以保护隐私）的多维度解释进行了实证与定性评估。我们为视频采用了多模态 CLIP 嵌入表示，通过改进的 LLM 辅助提示工程与池化评分机制来实现异常类型的识别。我们对用于空间异常热图定位的 Grad-ECLIP 进行了增强，利用我们新颖的提示引导分块自动聚焦机制兼顾了计算开销与准确度。此外，我们借助精细设计的提示工程，使用 VLM 生成所检测异常的上下文环境与因果推理描述。我们在多个公开 CCTV 和视频数据集上对 XAVAD 的准确性与计算成本进行了评估。实验表明，XAVAD 相比现有的前沿基线方法取得了显著提升，并揭示了 VAD 中 XAI 在隐私保护权衡方面的深刻见解。我们还进一步展示了消融实验及跨多种配置和建模替代方案的评估，以证实所提方法的有效性。代码开源地址：GitHub 链接 。

Abstract:Despite the promising development of deep learning for video anomaly detection (VAD), explainability (XAI) of these models remains a challenge. Explainable and privacy-protecting VAD builds transparency and trust that facilitates adoption of computer vision systems, including safety and security monitoring. However, anomaly explainability in privacy-preserving VAD systems is underexplored. In this study, we present the XAVAD approach, a unified descriptive anomaly XAI for an anonymized VAD, that integrates flexible zero-shot anonymization, anomaly type categorization, visual localization, and scene context description. We provide empirical and qualitative assessments of multifaceted explanations integrated with full-body visual anonymization for privacy protection. We adopt a multimodal CLIP embedding for videos, using improved LLM-assisted prompting and pool-scoring mechanisms for anomaly-type identification. We enhance Grad-ECLIP for spatial anomaly heatmap localization, leveraging computation and accuracy through our novel prompt-guided patching autofocus mechanism. We employ VLMs to generate context and reasoning descriptions of detected anomalies through curated prompt engineering. We have evaluated the accuracy and computational cost of XAVAD on several publicly available CCTV and video datasets. Our experiments demonstrate substantial gains of our XAVAD over state-of-the-art benchmark approaches and provide insights into privacy protection trade-offs in XAI for VAD. We further present ablation studies and assessments across diverse configurations and modeling alternatives to demonstrate the efficacy of our proposed methods. Code: this https URL}{this https URL .