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

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 matc h

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

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

Experiment Results

Experiment 1

Experiment 2 Wallflower 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