基于背景复杂度自适应距离阈值的修正SuBSENSE算法

针对自适应敏感度分割(self-balanced sensitivity segmenter, SuBSENSE)算法在真实复杂场景下距离阈值更新适应性差，导致检测效果不佳的问题，提出一种基于背景复杂度自适应距离阈值修正的SuBSENSE算法。结合时间一致性和空间一致性定义了一种背景复杂度的度量方式，以此为标准，通过距离阈值修正策略获取准确的距离阈值，以便获得更好的检测效果。本算法与像素自适应分割(based adaptive segmenter，PBAS)算法和传统SuBSENSE算法进行了对比。试验表明，在动态场景下，本算法获取的前景更加精确，精度比PBAS算法和传统SuBSENSE算法提高了6.70%和0.80%，召回率比PBAS算法和传统SuBSENSE算法分别提高了9.37%和1.24%。本算法优于对比算法，在动态场景下具有更高的鲁棒性和检测精度。

Modified SuBSENSE algorithm via adaptive distance threshold based on background complexity

In order to solve the problem of poor adaptability of SuBSENSE algorithm in updating distance threshold in real complex scenes, which resulted in poor detection effect, SuBSENSE algorithm is proposed based on adaptive distance threshold correction of background complexity. A measure of background complexity is defined based on temporal consistency and spatial consistency, and the distance threshold correction strategy to get the accurate distance threshold as a criterion to achieve better detection results. This algorithm was compared with PBAS and traditional SuBSENSE algorithm. Experiments showed that the prospects of the proposed algorithm were more accurate in dynamic scenarios. The precision of the proposed algorithm was 6.70% and 0.80% higher than that of the PBAS algorithm and the traditional SuBSENSE algorithm, and the recall was 9.37% and 1.24% higher than that of the PBAS algorithm and the traditional SuBSENSE algorithm, respectively. After a comprehensive study of the three indicators, it was found that the proposed algorithm was superior to the contrast algorithms, and had higher robustness and detection accuracy in dynamic scenarios.