Calibration-guided score fusion for robust multimodal traffic anomaly detection

Abstract

Multimodal traffic anomaly detection is affected by differences in visual and audio score ranges, temporal fluctuations, and unstable decision thresholds. This paper proposes a calibration-guided score fusion (CGSF) framework that processes video frames and audio spectrograms through separate reconstruction-based models. The resulting anomaly scores are temporally smoothed, normalized using validation data, and combined at the score level. A percentile estimated from normal validation samples is then used as the decision threshold. The framework was evaluated on the MAVD and DADA2000 datasets. On MAVD, CGSF achiev,,,,,,ed a ROC-AUC of 0.553, a PR-AUC of 0.082, and an F1-score of 0.129. It outperformed direct fusion in precision, recall, and F1-score, although the gain in ROC-AUC was small. Analysis on DADA2000 showed smoother temporal score behaviour after calibration and smoothing. The results indicate that CGSF mainly improves score comparability and threshold consistency rather than producing a large increase in detection accuracy. Its modular design also allows the visual and audio branches to be trained and updated independently.