STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video Anomaly Detection

• URL: http://arxiv.org/abs/2412.20084v1
• Date: Sat, 28 Dec 2024 08:49:23 GMT
• Title: STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video Anomaly Detection
• Authors: Zhangxun Li, Mengyang Zhao, Xuan Yang, Yang Liu, Jiamu Sheng, Xinhua Zeng, Tian Wang, Kewei Wu, Yu-Gang Jiang,
• Abstract summary: Video anomaly detection (VAD) has been extensively researched due to its potential for intelligent video systems. Most existing methods based on CNNs and transformers still suffer from substantial computational burdens. We propose a lightweight and effective Mamba-based network named STNMamba to enhance the learning of spatial-temporal normality.
• Score: 48.997518615379995
• License:
• Abstract: Video anomaly detection (VAD) has been extensively researched due to its potential for intelligent video systems. However, most existing methods based on CNNs and transformers still suffer from substantial computational burdens and have room for improvement in learning spatial-temporal normality. Recently, Mamba has shown great potential for modeling long-range dependencies with linear complexity, providing an effective solution to the above dilemma. To this end, we propose a lightweight and effective Mamba-based network named STNMamba, which incorporates carefully designed Mamba modules to enhance the learning of spatial-temporal normality. Firstly, we develop a dual-encoder architecture, where the spatial encoder equipped with Multi-Scale Vision Space State Blocks (MS-VSSB) extracts multi-scale appearance features, and the temporal encoder employs Channel-Aware Vision Space State Blocks (CA-VSSB) to capture significant motion patterns. Secondly, a Spatial-Temporal Interaction Module (STIM) is introduced to integrate spatial and temporal information across multiple levels, enabling effective modeling of intrinsic spatial-temporal consistency. Within this module, the Spatial-Temporal Fusion Block (STFB) is proposed to fuse the spatial and temporal features into a unified feature space, and the memory bank is utilized to store spatial-temporal prototypes of normal patterns, restricting the model's ability to represent anomalies. Extensive experiments on three benchmark datasets demonstrate that our STNMamba achieves competitive performance with fewer parameters and lower computational costs than existing methods.

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