STEP: Score-Based Temporal Energy for Human Pose Video Anomaly Detection

2026-08-20

AI Summary

为解决视频异常检测中噪声注入导致的物理姿态不真实问题，提出STEP框架，利用PCA投影姿态序列，并结合置信度分数加权机制，提高长视频序列处理性能。

Abstract

Skeleton-based Video Anomaly Detection (VAD) offers a robust, privacy-preserving solution for identifying abnormal behaviors. To model the distribution of normal static and moving poses, recent methods train Energy-Based Models (EBMs) via Denoising Score Matching (DSM). However, directly injecting noise, required for training, into raw joint coordinates creates physically impossible poses, and this structural collapse severely worsens as the temporal window expands. To address this, we introduce STEP, a simple framework that utilizes Principal Component Analysis (PCA) to project pose sequences into a compact, whitened PC-space. Learning the data density within this well-behaved PC-space ensures that the injected noise translates into physically plausible variations, which allows the model to process longer video sequences without the performance collapse of raw coordinate baselines. Additionally, to mitigate inherent pose estimation inaccuracies arising from occlusions or motion blur, we integrate a sequence-level weighting mechanism based on the estimator's confidence scores. Operating at real-time computational efficiency, our simple and lightweight framework outperforms the previous skeleton-based state-of-the-art by 12.2% (90.1% AUROC) on the challenging UBnormal dataset and achieves highly competitive results by improving on the ShanghaiTech benchmark.

Problem

• Video Anomaly Detection
• Energy-Based Models
• Denoising Score Matching
• Principal Component Analysis
• Pose Estimation

Innovation

• Score-Based Temporal Energy
• Principal Component Analysis (PCA)
• Physically Plausible Variations
• Sequence-Level Weighting Mechanism