MemoVAD: 边缘计算场景下基于动态语义记忆的资源高效视频异常检测
MemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarios
AI总结：
提出MemoVAD边缘-云协同框架，通过不确定性感知门控策略选择性调用云端视觉语言模型，并设计动态语义记忆缓存原型，在降低通信开销的同时提升视频异常检测性能。
摘要：
在真实监控场景中部署视频异常检测（VAD）面临着对高层语义的需求以确保有效性，与边缘设备有限计算资源之间的根本矛盾。视觉语言模型（VLM）提供了丰富的开放词汇语义，但其延迟和计算成本阻碍了设备端部署。为解决这一挑战，我们提出MemoVAD，一种边缘-云协同框架，选择性地将VLM语义融入流式VAD。MemoVAD在边缘端使用轻量级检测器和因果时序上下文编码器（TCE）建模时序依赖，运行大部分推理。具体而言，我们引入基于主观逻辑的不确定性感知门控（UAG）策略，以建模感知不确定性，并仅对高不确定性和语义新颖的片段查询云端VLM。此外，设计动态语义记忆（DSM）缓存经VLM验证的原型以实现高效检索，使边缘模型通过语义适配器逐步融入VLM级语义。在真实边缘设备上对UCF-Crime和XD-Violence数据集的实验表明，MemoVAD在显著降低通信开销的同时，超越了当前最优性能。
英文摘要：
Deploying Video Anomaly Detection (VAD) in real-world surveillance faces a fundamental tension between the demand for high-level semantics to ensure effectiveness and the limited computational resources of edge devices. Vision-Language Models (VLMs) provide rich open-vocabulary semantics, but their latency and computational cost preclude on-device deployment. To address the challenge, we propose MemoVAD, an edge-cloud collaborative framework that selectively incorporates VLM semantics into streaming VAD. MemoVAD runs most inference on the edge with a lightweight detector and a causal Temporal Context Encoder (TCE) to model temporal dependencies. Specifically, we introduce an Uncertainty-Aware Gating (UAG) policy grounded in Subjective Logic to model perceived uncertainty and query the cloud-based VLM only for high-uncertainty and semantically novel clips. Besides, a Dynamic Semantic Memory (DSM) is designed to cache VLM-verified prototypes for efficient retrieval, enabling the edge model to progressively incorporate VLM-level semantics via a semantic adapter. Experiments on UCF-Crime and XD-Violence datasets via a real edge device show that MemoVAD substantially reduces communication overhead while surpassing state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
