Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (12): 22-27.doi: 10.16180/j.cnki.issn1007-7820.2020.12.005

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Improved Background Subtraction Based on Feature Fusion

GUAN Hongyun,SU Zhentao,WANG Chen   

  1. School of Information Science and Technology,Donghua University,Shanghai 201620,China
  • Received:2019-09-18 Online:2020-12-15 Published:2020-12-22
  • Supported by:
    National Natural Science Foundation of China(61772130)


The background difference method has higher reliability in completely and quickly segmenting the target image, but the detection effect is not good in the case of background disturbance and illumination variation. This study proposes an improved background difference algorithm based on feature fusion. This algorithm fuses spatio-temporal local binary pattern texture features and color features, and consideres the confidence and similarity scores of the two features to obtain the background probability, following by foreground segmentation. The background pixels are used for background template updates, which is designed to better solve the problem of target detection in complex backgrounds. The experimental results show that the detection effect of this algorithm is better than other similar algorithms. While maintaining the robustness and complexity of the background difference algorithm, it displayes good detection effect under the background disturbance and illumination changes.

Key words: background subtraction, target detection, feature fusion, confidence, background probability, template update

CLC Number: 

  • TP391.41