Lightweight Multimodal Anomaly Detection Based on GhostNet and FiLM

Abstract

To address the trade-off between detection accuracy and deployment efficiency in surveillance-based anomaly detection, we propose a lightweight multimodal abnormal behavior detection method based on GhostNet and FiLM fusion (G-FLM). GhostNet is employed as a lightweight visual backbone to reduce computational redundancy, while audio features are introduced to generate FiLM-based channel-wise affine parameters to generate FiLM-based channel-wise affine parameters for conditional recalibration of video features, thereby achieving effective audio-video information fusion with limited additional overhead. Experimental results on the XD-Violence dataset show that, under the same experimental configuration, G-FLM achieves an mAP of 0.7392. Compared with multimodal baselines such as Two-Stream and I3D, the mAP is improved by 5.6% and 18.0%, respectively. Precision reaches 0.8163 and F1 reaches 0.7260, both outperforming the comparison methods. At the same time, the model has only 3.63M parameters, requires only 1.9054 GFLOPs, and achieves an inference latency of 30.53 ms/batch, achieving higher detection accuracy with substantially lower computational overhead than the compared baselines. The results indicate that G-FLM achieves a good overall balance between detection accuracy and deployment efficiency, and provides a feasible lightweight solution for multimodal abnormal behavior detection in resource-constrained scenarios.