Fusing Self-Organized Neural Network and Keypoint Clustering for Localized Real-Time Background Subtraction

Int J Neural Syst. 2020 Apr;30(4):2050016. doi: 10.1142/S0129065720500161. Epub 2020 Mar 2.


Moving object detection in video streams plays a key role in many computer vision applications. In particular, separation between background and foreground items represents a main prerequisite to carry out more complex tasks, such as object classification, vehicle tracking, and person re-identification. Despite the progress made in recent years, a main challenge of moving object detection still regards the management of dynamic aspects, including bootstrapping and illumination changes. In addition, the recent widespread of Pan-Tilt-Zoom (PTZ) cameras has made the management of these aspects even more complex in terms of performance due to their mixed movements (i.e. pan, tilt, and zoom). In this paper, a combined keypoint clustering and neural background subtraction method, based on Self-Organized Neural Network (SONN), for real-time moving object detection in video sequences acquired by PTZ cameras is proposed. Initially, the method performs a spatio-temporal tracking of the sets of moving keypoints to recognize the foreground areas and to establish the background. Then, it adopts a neural background subtraction, localized in these areas, to accomplish a foreground detection able to manage bootstrapping and gradual illumination changes. Experimental results on three well-known public datasets, and comparisons with different key works of the current literature, show the efficiency of the proposed method in terms of modeling and background subtraction.

Keywords: Self-organized neural network; background modeling; clustering; foreground detection.

MeSH terms

  • Cluster Analysis
  • Datasets as Topic
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Models, Theoretical
  • Neural Networks, Computer*
  • Pattern Recognition, Automated / methods*
  • Subtraction Technique*