Background:
ViBE (Visual Background Extractor) is an algorithm with a variety of advantages
in video moving object detection which utilizes a pixel-level background modeling. However,
it is not suitable for distinguishing the scene of drastic change, adapts poorly to the sudden
change of the illumination and may lost the object easily, because this algorithm uses a fixed threshold
to distinguish the background from the foreground.
Methods:
In this paper, an improved ViBE algorithm is proposed, which an adaptive dynamic
threshold method is introduced for classification of the foreground and the background in the changing
scenes. When reconstructing the model it required for drastic change of illumination, Otsu algorithm
is used to judge the threshold and select the appropriate frame to complete the reconstruction
to achieve quick adapt to the light.
Results:
Experimental results show that the proposed algorithm has higher recall value, better precision
and F value comparing to the original algorithm. The improved algorithm has the highest classification
accuracy among other similar algorithms and therefore the improved algorithm significantly
improves the detection results.
Conclusion:
After analyzing the principle of ViBE algorithm, this paper proposed improvements to
it from two aspects to aim at its deficiency. Taking into account of the dynamic changes of different
environments, the change factor was proposed to measure the dynamic degree of background. According
to the value of the factor, adaptive clustering was obtained and clustering threshold was updated to
improve the adaptability of the algorithm to the dynamic environments. The improved ViBE algorithm
can find the appropriate frame to reconstruct model structure in the case of abrupt light change, which
can quickly adapt itself to the light change and be more accurate in the object detection.
FPGA Implementation of Moving Object and Face Detection Using Adaptive Threshold
