提出VideoPatchCore，无需训练即可高效记忆正常视频特征
VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection
- 基于PatchCore思想设计三类视频专用记忆结构
- 无训练、无需联合优化，实现与顶尖方法相当的检测性能
- 适合希望快速部署异常检测系统的开发者使用
视频异常检测（VAD）是计算机视觉中视频分析与监控的关键任务。当前方法依赖记忆模块存储正常帧特征，通过重建帧与输入帧的差异识别异常。然而，该方法需同时优化记忆模块与编码器-解码器模型，带来优化难度大、实现复杂及性能随记忆规模波动等问题。为此，我们提出VideoPatchCore，受PatchCore启发，设计优先优化记忆的结构，并配置三类针对视频数据特性的记忆类型。该方法有效克服现有记忆方法的局限，性能媲美最先进方法。此外，本方法无需训练，实现简单，使VAD更易部署。代码已公开于github.com/SkiddieAhn/Paper-VideoPatchCore。
原文摘要 · Abstract (English)
Video anomaly detection (VAD) is a crucial task in video analysis and surveillance within computer vision. Currently, VAD is gaining attention with memory techniques that store the features of normal frames. The stored features are utilized for frame reconstruction, identifying an abnormality when a significant difference exists between the reconstructed and input frames. However, this approach faces several challenges due to the simultaneous optimization required for both the memory and encoder-decoder model. These challenges include increased optimization difficulty, complexity of implementation, and performance variability depending on the memory size. To address these challenges,we propose an effective memory method for VAD, called VideoPatchCore. Inspired by PatchCore, our approach introduces a structure that prioritizes memory optimization and configures three types of memory tailored to the characteristics of video data. This method effectively addresses the limitations of existing memory-based methods, achieving good performance comparable to state-of-the-art methods. Furthermore, our method requires no training and is straightforward to implement, making VAD tasks more accessible. Our code is available online at github.com/SkiddieAhn/Paper-VideoPatchCore.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。