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山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (3): 38-44.doi: 10.6040/j.issn.1672-3961.0.2019.413

• 机器学习与数据挖掘 • 上一篇    下一篇

基于背景复杂度自适应距离阈值的修正SuBSENSE算法

成科扬1,2(),孙爽1,詹永照1   

  1. 1. 江苏大学计算机科学与通信工程学院，江苏 镇江 212013
    2. 社会安全风险感知与防控大数据应用国家工程实验室，北京 100846
  • 收稿日期:2019-07-22 出版日期:2020-06-20 发布日期:2020-06-16
  • 作者简介:成科扬(1982—),男,江苏南通人,副教授,博士,主要研究方向为模式识别,计算机视觉. E-mail: kycheng@ujs.edu.cn
  • 基金资助:
    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

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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摘要：

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。

关键词: SuBSENSE算法, 前景检测, 距离阈值修正, 背景复杂度

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

中图分类号: 

  • TP391

图1

PBAS算法、SuBSENSE算法和本研究算法的对比试验"

表1

召回率指标对比结果"

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

表2

精度指标对比结果"

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

表3

F1指标对比结果"

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

图2

3种算法不同评价指标对比"

表4

各算法综合对比"

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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