Moving objects detection based on tensor ring low rank decomposition

The advancement of high-quality camera technology has increased the demand for efficient video analysis methods. Current methods mostly rely on matrix-based approaches, which break data structures and lose some spatial information. This paper proposes a novel approach (TRLRTTV) that combines Low Rank Tensor Ring decomposition and Tensor Total Variation regularization for moving objects detection (MOD). For static background detection, the tensor ring (TR) decomposition is utilized to extract low rank information, and low rank assumption is placed on tensor factors instead of the original data. For moving objects, a tensor total variation model with [Formula: see text] regularization is employed to ensure the representation of foreground information along the spatio-temporal direction. The results demonstrate that, compared to existing methods, the proposed algorithm achieves a 3%-8% performance improvement in background separation and the comprehensive performance metric f. Furthermore, the proposed method ensures robustness against noise interference, such as Gaussian and salt-and-pepper noise and more suitable for higher-dimensional video processing.