VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection
This work addresses optimization difficulties in video anomaly detection for surveillance applications, offering a more accessible and straightforward implementation, though it is incremental as it builds on existing memory techniques.
The paper tackled the challenge of simultaneous optimization in memory-based video anomaly detection by proposing VideoPatchCore, a method that prioritizes memory optimization with three tailored memory types, achieving performance comparable to state-of-the-art methods without requiring training.
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.