1 / 16

Kernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background

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.

xanthe
Download Presentation

Kernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Kernel Similarity Modeling of Texture Pattern Flow for Motion Detection in Complex Background Baochang Zhang, YongshengGao, Sanqiang Zhao, BinengZhong 2011 IEEE transection on CSVT

  2. Outline • TPF Operator • Kernel Similarity Modeling • Experiment Result • Conclusion

  3. 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

  4. 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

  5. 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

  6. Flowchart for one pixel

  7. Integral Histogram

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. Experiment 1

  14. Experiment 2 Wallflower video GMM CMU LBP TPF KSM-TPF

  15. Experiment 2 GMM CMU LBP TPF KSM-TPF

  16. Conclusion • KSM-TPF is much more robust to significant background variations • However, it is less computationally efficient than the GMM method or LBP method

More Related