An Effective Subsuperpixel-Based Approach for Background Subtraction

Fast-D: When Non-Smoothing Color Feature Meets Moving Object Detection in Real-Time

“…The local binary similarity pattern (LBSP) was a combination of intra-LBSP and inter LBSP used in [20], [21], which increased accuracy as well as complexity. The segmentation accuracy and complexity of the methods proposed by Hofmann et al [19] and and St. Charles et al [20], [21] was high compared with methods [9]- [17]. However, we see that none paid attention to find the trade-off between accuracy and complexity of the segmentation.…”

“…MOG [11] GMM [12] KNN [13] SBS [17] SuperBE [18] MOG2 [26] Cuevas [27] Berjón [28] Haines [29] SC-SOBS [30] WNN [31] CNN [32] DeepBS [33] RB-SOM [34] MSFgNet [35] MS-ST [36] BMN-BSN [37] FIGURE 2. A taxonomy of background subtraction-based moving object detection approach.…”

“…Overall, our method outperformed compared with the existing approaches. Fast-D -0.9961 0.0039 0.0286 0.5155 0.9714 0.9334 0.9520 SuBS [21] 2015 0.9962 0.0038 0.0405 0.5738 0.9594 0.9335 0.9462 SBS [17] 2019 0.9975 0.0024 0.0014 0.3862 0.9170 0.8672 0.8914 PBAS [19] 2012 0.9981 0.0019 0.1872 1.1500 0.8128 0.9585 0.8797 LOB [20] 2014 0.9980 0.0020 0.1893 1.1910 0.8107 0.9565 0.8776 ViBe [15] 2011 0.9939 0.0060 0.0018 0.7800 0.8950 0.7209 0.7986 SuperBE [18] 2019 0.9994 0.0005 0.0105 1.0844 0.5343 0.9552 0.6852 Table 5 shows the comparison of our proposed approach with the existing methods in terms of F1-score on CD-2012 and CD-2014 datasets. As we mentioned before, F1-score is the most important metric to evaluate the accuracy of BG/FG segmentation.…”

“…Later on, SACON's sample consensus policy was used by methods [15], [16] to improve classification accuracy. Recently, Chen et al [17] proposed sub-superpixel-based background subtraction (SBS) to increase detection accuracy. Additionally, in superpixel-level background estimation (SuperBE) [18], superpixel-based segmentation was proposed to reduce complexity.…”

“…However, to reduce the complexity, SuperBE considerably sacrificed segmentation accuracy. Though the algorithmic complexity of the above BGS methods [9]- [17] was low, the segmentation accuracy was very poor. Afterward, Hofmann et al [19] proposed a pixel-based adaptive segmenter (PBAS), newly created the dynamic background modeling policy, and used a sample consensus policy and gradient magnitude feature in addition to color feature.…”