Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (3): 38-44.doi: 10.6040/j.issn.1672-3961.0.2019.413

• Machine Learning & Data Mining • Previous Articles     Next Articles

Modified SuBSENSE algorithm via adaptive distance threshold based on background complexity

Keyang CHENG1,2(),Shuang SUN1,Yongzhao ZHAN1   

  1. 1. School of Computer Science and Telecommunications Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
    2. National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data, Beijing 100846, China
  • Received:2019-07-22 Online:2020-06-20 Published:2020-06-16
  • Supported by:
    国家自然科学基金资助项目(61972183);国家自然科学基金资助项目(61602215);社会安全风险感知与防控大数据应用国家工程实验室主任基金项目

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Abstract:

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.

Key words: SuBSENSE algorithm, foreground detection, modified distance threshold, background complexity

CLC Number: 

  • TP391

Fig.1

Comparative experiment of PBAS, SuBSENSE and Ours"

Table 1

The comparison of recall evaluation %"

dataset PBAS SuBSENSE Ours
overpass 67.04 78.52 79.07
canoe 56.25 65.90 67.62
fountain01 86.38 87.71 89.13
fountain02 90.77 92.32 93.48
fall 94.74 85.67 85.67
boats 22.13 55.96 58.53
overall 69.55 77.68 78.92

Table 2

The comparison of precision evaluation %"

dataset PBAS SuBSENSE Ours
overpass 96.90 94.37 94.98
canoe 99.86 99.33 99.45
fountain01 27.51 65.99 67.64
fountain02 96.51 96.58 96.89
fall 80.67 87.58 88.14
boats 98.08 91.06 92.63
overall 83.26 89.15 89.96

Table 3

The of F1 evaluation %"

dataset PBAS SuBSENSE Ours
overpass 79.25 85.72 86.30
canoe 71.96 79.23 80.50
fountain01 41.73 75.31 76.91
fountain02 93.55 94.41 95.16
fall 87.14 86.61 86.89
boats 36.11 69.32 71.73
overall 68.29 81.77 82.92

Fig.2

Comparisons of different evaluation indexes ofthree algorithms"

Table 4

The overall comparison of three algorithms"

Methods Recall/
%		 Precise/
%		 F-Measure/
%		 处理帧率/
(帧·s-1)
PBAS 69.55 83.26 68.29 37
SuBSENSE 77.68 89.15 81.77 30
Ours 78.92 89.96 82.92 27
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