Efficient ghost removal in motion detection with patch-corrected background differentiation
Yuri Boiko, Pierre Payeur · Optical Memory and Neural Networks · 2011
An efficient ghost removal technique is proposed as an extension to adaptive background differentiation for motion detection. The pixels of the first frame in the sequence representing moving objects are replaced with the values taken for the same pixels from the memory bank where those pixels are identified as non-moving ones. The memory bank is built of the frames immediately following or, alternatively preceding, the initial frame of the analyzed sequence. This allows creating the initial background model with no moving pixels. Parameters optimization is conducted for specific case of traffic control system application. Experiments demonstrate that threshold reduction is beneficial to achieve completeness of the ghost removal. Additionally, a second improvement is introduced to reduce for the noise by non-stationary cameras which are shown to be efficiently compensated by a second derivative in the temporal differentiation when working with videos at a sufficiently high frame rate.