发表机构
Xidian University; Hunan University（西安电子科技大学; 湖南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无监督视频异常检测，提出关系感知的INTERACT框架，通过联合建模实体外观、运动与空间关系，实现高精度异常识别，在ShanghaiTech上取得84.5%的帧级AUC。
AI 中文摘要
巡逻机器人和监控系统的视频异常检测必须识别熟悉实体之间的异常交互。当单个实体看起来正常但其空间或运动关系异常时，现有的像素重建和孤立实体方法可能会失效。本工作提出了交互中心网络用于时间实体关系分析和一致性测试（INTERACT），这是一个用于无监督视频异常检测的框架。人物-物体外观-运动交互（POAMI）通过联合建模每帧中人物和物体的外观、运动及空间配置，超越了孤立实体编码。生成的表示同时捕获单个实体状态及其跨实体上下文。基于这些表示，目标几何引导的关系交互预测（TGRIP）从历史关系记忆和目标帧几何预测目标实体交互状态，无需显式身份跟踪。运动-交互重建与对齐（MIRA）通过条件流重建和语义一致性检查评估这些预测，提供超越预测误差的互补异常证据。INTERACT达到了最先进的性能，在ShanghaiTech基准上获得了84.5%的帧级AUC。消融研究表明，移除跨实体注意力会导致最大的性能下降，证明了关系建模的必要性。INTERACT对于由人物和物体之间关系变化引起的异常特别有效，同时在一般场景中保持有竞争力的性能。源代码将在该https URL公开提供。
英文摘要
Video anomaly detection for patrol robots and surveillance systems must recognize abnormal interactions among familiar entities. Existing pixel-reconstruction and isolated-entity methods may fail when individual entities appear normal but their spatial or motion relations are abnormal. This work presents Interaction-Centric Network for Temporal Entity-Relation Analysis and Consistency Testing (INTERACT), a framework for unsupervised video anomaly detection. Person-Object Appearance-Motion Interaction (POAMI) moves beyond isolated-entity encoding by jointly modeling the appearance, motion, and spatial configurations of persons and objects in each frame. The resulting representations capture both individual entity states and their cross-entity context. Based on these representations, Target Geometry-Guided Relational Interaction Prediction (TGRIP) predicts target entity interaction states from historical relational memory and target-frame geometry without explicit identity tracking. Motion-Interaction Reconstruction and Alignment (MIRA) then evaluates these predictions through conditional flow reconstruction and semantic consistency checking, providing complementary anomaly evidence beyond prediction error alone. INTERACT achieves state-of-the-art performance, obtaining a frame-level AUC of 84.5% on the ShanghaiTech benchmark. Ablation studies show that removing cross-entity attention causes the largest performance drop, demonstrating the necessity of relational modeling. INTERACT is particularly effective for anomalies caused by changes in relations among people and objects, while maintaining competitive performance in general scenarios. The source code will be made publicly available at https://github.com/ppworkhard/INTERACT.
CommentsThe source code will be made publicly available at https://github.com/ppworkhard/INTERACT
