Abstract:

Video anomaly detection currently faces several challenges. First， insufficient integration of appearance and motion information in complex environments results in a lack of semantic associations between the two modalities. Second， excessive reliance on prior information weakens the model's capacity for effective feature representation. Therefore， an Appearance-Motion Collaborative modeling for Video Anomaly Detection （AMC-VAD） method was proposed. It achieved pixel-level appearance-motion feature weight adjustment through a Pixel-level Dynamic Adaptation （PDA） module， used a dual-branch DepthWise Separable Convolution （DWSConv） to extract multi-scale semantic information， and enhanced the semantic relevance of feature fusion through dynamic activation and residual connection. In addition， an Auxiliary Memory Module （AMM） was designed to extract prototype features from a memory pool via a query-driven semantic alignment strategy， and a Dynamic Aggregation Mechanism （DAM） was incorporated to enhance the query feature saliency representations， alleviating the feature weakening caused by prior information coverage. A diversity loss was introduced to reduce redundancy in memory item distribution， thereby enhancing the model's discriminative ability for abnormal patterns. Experimental results showed that the proposed method achieved Area Under the receiver operating Characteristic curve （AUC） of 98.5% and 88.5% on the UCSD Ped2 and CUHK Avenue datasets， respectively， outperforming AMMC-Net （Appearance-Motion Memory Consistency Network） by 1.9 and 1.9 percentage points， respectively. The above validates the effectiveness of the proposed method in complex dynamic scenarios.

Key words: video anomaly detection, appearance-motion coordination, semantic association, auxiliary memory, diversity loss

摘要：

视频异常检测目前面临以下挑战：一是在复杂环境中外观和运动信息融合不足，导致二者之间的语义关联缺失；二是模型过度依赖先验信息，导致有效特征表达能力弱化。因此，提出基于外观-运动协同建模的视频异常检测（AMC-VAD）方法。该方法通过像素级动态适配（PDA）模块实现外观与运动特征的逐像素权重调控，利用双分支深度可分离卷积（DWSConv）提取多尺度语义信息，并通过动态激活与残差连接增强特征融合的语义关联性；设计辅助记忆模块（AMM），采用查询驱动的语义对齐策略从记忆池提取原型特征，并结合动态聚合机制（DAM）强化查询特征的显著性表达，缓解先验信息覆盖导致的特征弱化；引入多样性损失函数优化记忆项分布的冗余性，提升模型对异常模式的判别能力。实验结果表明，AMC-VAD方法在UCSD Ped2和CUHK Avenue数据集上的接受者操作特征曲线下面积（AUC）分别为98.5%和88.5%，比AMMC-Net（Appearance-Motion Memory Consistency Network）分别提升了1.9和1.9个百分点，验证了该方法在复杂动态场景下的有效性。

关键词: 视频异常检测, 外观-运动协同, 语义关联, 辅助记忆, 多样性损失