kernel similarity modeling of texture pattern flow for motion detection in complex background
DESCRIPTION
Kernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background. Baochang Zhang, Yongsheng Gao , Sanqiang Zhao, Bineng Zhong. 2011 IEEE transection on CSVT. Outline. TPF Operator Kernel Similarity Modeling Experiment Result Conclusion. - PowerPoint PPT PresentationTRANSCRIPT
Kernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background
2011 IEEE transection on CSVT
Baochang Zhang, Yongsheng Gao, Sanqiang Zhao, Bineng Zhong
Outline TPF Operator Kernel Similarity Modeling Experiment Result Conclusion
TPF Operator-Spatial Given a gray-scale image sequence To capture the spatial variations in x and
y directions, two threshold functions, and , are employed to encode the gradient information into binary representations
TPF Operator-Temporal The temporal derivative is defined as
A pixel value lying within 2.5 standard deviations of a distribution is defined as a match
match
TPF Operator By integrating both spatial and temporal
information, the TPF is defined as
TPF reveals the relationship between derivative directions in both spatial and temporal domains
Flowchart for one pixel
Integral Histogram
Integral Histogram of TPF
Using a neighborhood region provides certain robustness against noise
When the local region is too large, the more details will be lost
Building Background Model Use GMM to model the background If a match has been found for the pixel,
update mean and variance of the matched Gaussian distribution
If none of the K Gaussian distributions match the current pixel value, the least probable distribution is replaced with a new distribution whose mean is the current pixel value
Kernel Similarity Measurement
We use k to represent the result of kernel similarity
With the information of kernel similarity, we can get an adaptive threshold to classify the input pixel
: mean of the th Gaussian distribution at time t: variance of the th Gaussian distribution at time t : model integral histogram : learning rate
Update the Background Model
If the pixel is labeled as background, the background model histogram with the highest similarity value will be updated with the new data
: input integral histogram : 1 for the best-matched distribution, 0 for the other distributions
Experiment Results All the experiments in this paper are
conducted on gray-level values For simplicity, 3 Gaussian distributions
and 3 model integral histograms are used to describe all the Gaussian mixture models
= 0.7, = 0.01
Experiment 1
Experiment 2Wallflower video(a)GMM(b)CMU(c) LBP(d)TPF(e)KSM-TPF
Experiment 2
GMM CMU LBP TPF KSM-TPF
Conclusion KSM-TPF is much more robust to
significant background variations However, it is less computationally
efficient than the GMM method or LBP method