Reliability-Aware Prototype Calibration for Frozen Pose-Flow Video Anomaly Detection

Pose-flow video anomaly detectors are attractive for one-class surveillance because they provide likelihood-based rankings for tracked skeleton windows. However, a single likelihood score may hide multimodal normal behavior and be sensitive to pose-observation noise. We study a frozen-detector setting in which the pose-flow backbone, cached skeleton tracks, and evaluation pipeline are fixed. Reliability-Aware Prototype Calibration (RPC) is a post-hoc score calibration method for this setting. It adds a standardized nearest-prototype deviation in the frozen latent space to the standardized flow score, and uses keypoint confidence only to gate this added geometric evidence. Thus, RPC preserves the original density signal while correcting the ranking with empirical normal-mode structure under pose reliability. Across two frozen pose-flow backbones and four datasets, RPC improves frame-level AUROC in all eight backbone-dataset pairs, with gains ranging from 0.34 to 4.49 percentage points and averaging 2.03 points. Ablation and reliability analyses show that prototype deviation is the main corrective signal, while reliability gating is most useful when pose observations are less trustworthy. These results suggest that lightweight post-hoc calibration can strengthen cached pose-flow systems when retraining or reproducing the full pose pipeline is impractical.

Augmented Set-membership Affine Projection Algorithm and Its Performance Analysis

The augmented affine projection algorithm (AAPA) has considerably excellent performance for highly colored input signals. However, the direct matrix inversion operation leads to a high computational complexity, especially with high projection order. Inspired by the excellent characteristics of set-membership filtering (SMF), this paper proposes the augmented set-membership affine projection algorithm (ASM-APA), which not only has low computational complexity but also offers improved performance compared with AAPA. Then, the computational complexity and stability of ASM-APA are analyzed, and the condition for maintaining the stability of the algorithm is provided. Finally, in the computer simulation phase, the results of the simulation experiments demonstrated that ASM-APA has superior performance compared to AAPA.