Abstract:

Compared with the traditional background subtraction model, the VIBE algorithm does not require an estimation of a probability function for background pixels or a sequence of training samples. So there is a good balance between computation complexity and accuracy performance for the algorithm. It can be usually used in real time video embedded sequence surveillance systems. The resolutions of a video sequence can be changed under some circumstances. However, the fixed parameters method adopted in the traditional VIBE leads to degraded detection accuracy. We present an improved VIBE algorithm which obtains optimal parameters using the QPOS algorithm during initialization in order to improve the generalization ability of the VIBE. Experimental results verify the proposed algorithm quantitatively and qualitatively, which can greatly improve the accuracy performance.

Key words: visual background extractor, background subtraction, quantum-behavior particle swarm optimalization