The Self-Organizing Background Subtraction (SOBS) algorithm implements an approach to moving object detection based on the neural background model automatically generated by a self-organizing method, without prior knowledge about the involved patterns. Such adaptive model can handle scenes containing moving backgrounds, gradual illumination variations and camouflage, can include into the background model shadows cast by moving objects, and achieves robust detection for different types of videos taken with stationary cameras. Moreover, the introduction of spatial coherence into the background update procedure leads to the so-called SC-SOBS algorithm, that provides further robustness against false detections. The paper includes extensive experimental results achieved by the SOBS and the SC-SOBS algorithms on the dataset made available for the Change Detection Challenge at the IEEE CVPR2012.

The SOBS algorithm: What are the limits?

MADDALENA, LUCIA;PETROSINO, Alfredo
2012

Abstract

The Self-Organizing Background Subtraction (SOBS) algorithm implements an approach to moving object detection based on the neural background model automatically generated by a self-organizing method, without prior knowledge about the involved patterns. Such adaptive model can handle scenes containing moving backgrounds, gradual illumination variations and camouflage, can include into the background model shadows cast by moving objects, and achieves robust detection for different types of videos taken with stationary cameras. Moreover, the introduction of spatial coherence into the background update procedure leads to the so-called SC-SOBS algorithm, that provides further robustness against false detections. The paper includes extensive experimental results achieved by the SOBS and the SC-SOBS algorithms on the dataset made available for the Change Detection Challenge at the IEEE CVPR2012.
978-1-4673-1612-5
978-1-4673-1611-8
978-1-4673-1610-1
978-1-4673-1612-5
978-1-4673-1611-8
978-1-4673-1610-1
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/32340
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 228
  • ???jsp.display-item.citation.isi??? ND
social impact