Abstract. Subtraction techniques are used to distinguish moving objects or foregrounds that are being tracked from a static background. To prevent possible local misclassifications, the subspace spanned by low-rank features computed from a multilinear singular value decomposition (MLSVD), can be used to filter out noises or gradual changes from the background. However, as it is prohibitively expensive to compute a new MLSVD from scratch at every iteration, we propose an adaptive and efficient method for updating an MLSVD by reusing previous decompositions while tracking more accurate decomposition errors. The experimental results reveal that the proposed MLSVD update algorithm exhibits a faster execution speed and better accuracy than other MLSVD update algorithms used in background subtraction applications.

中文翻译：

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背景减法应用中多线性奇异值分解的有效更新

摘要。减法技术用于区分正在跟踪的移动对象或前景与静态背景。为了防止可能的局部错误分类，由多线性奇异值分解 (MLSVD) 计算出的低秩特征所跨越的子空间可用于过滤掉背景中的噪声或逐渐变化。然而，由于在每次迭代时从头开始计算新的 MLSVD 成本高得令人望而却步，我们提出了一种自适应且有效的方法，通过重用以前的分解同时跟踪更准确的分解错误来更新 MLSVD。实验结果表明，与背景减法应用中使用的其他 MLSVD 更新算法相比，所提出的 MLSVD 更新算法具有更快的执行速度和更好的准确性。