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Real-Time Compressive Tracking

Real-Time Compressive Tracking. Gao Yuefang 2013.04.16. some slides are from Kaihua Zhang. Outline. Paper & Author information Online Visual tracking Introduction Compressive Tracking Algorithm Experimental results Questions. Paper & Author information.

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Real-Time Compressive Tracking

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  1. Real-Time Compressive Tracking Gao Yuefang 2013.04.16 some slides are from Kaihua Zhang

  2. Outline • Paper & Author information • Online Visual tracking Introduction • Compressive Tracking Algorithm • Experimental results • Questions

  3. Paper & Author information • Paper: Real-Time Compressive Tracking, ECCV2012 • Authors: • Kaihua Zhang • PhD, Depart. Of Computing, the Hong Kong Polytechnic University • http://www.comp.polyu.edu.hk/~cskhzhang/ • Lei Zhang • Associate Professor, Depart. Of Computing, the Hong Kong Polytechnic University • http://www4.comp.polyu.edu.hk/~cslzhang/ • Ming-Hsuan Yang • Assistant professor, Electrical Engineering and Computer Science, • University of California, Merced http://faculty.ucmerced.edu/mhyang/

  4. Outline • Paper & Author information • Online Visual tracking Introduction • Compressive Tracking Algorithm • Experimental results • Questions

  5. Online Visual tracking Introduction • Invariant feature detectors (e.g. SIFT,SURF) • Online learning (e.g. Boosting, MIL) • Object detection (e.g. HOG for people detection) Online Visual tracking / Tracking by Detection

  6. Online Visual tracking Introduction (cont’) • One-shot learning for the first frame • learn and update appearance model during tracking

  7. Online Visual tracking Introduction (cont’) Online Tracking methods: • Generative methods • learn a model to represent the object and then use it to search for the image with minimal reconstruction error. • Discriminative methods • pose tracking problem as a binary classification task in order to find the decision boundary for separating the object from the background. • Collaborative methods

  8. Outline • Paper & Author information • Online Visual tracking Introduction • Compressive Tracking Algorithm • Experimental results • Questions

  9. Compressive Tracking Algorithm Existing Online Tracking methods often have the following problems: Online appearance models are data-dependent. As a result of self-taught learning, the mis-aligned samples are likely to be added and degrade the appearance model. How to solving the above problems? • appearance model based on data-independent, which employs non-adaptive random projections and preserves the structure of the image feature space. • a very sparse measurement matrix is adopted to efficiently extract the features for the appearance model

  10. Compressive Tracking Algorithm (con’t) Paper content: • Appearance model based data-independent • Multi-scale image feature representation; • Random projection of above image features; • Classifier • Naïve Bayes Classifier • Appearance model and classifier updating

  11. Compressive Tracking Algorithm (con’t)

  12. Compressive Tracking Algorithm (con’t) Main Steps: • Multi-scale image feature representation • The dimensionality of X is very high.

  13. Compressive Tracking Algorithm (con’t) • How to choose the random matrix R? R should satisfy the following two properties: • Restricted isometry property(RIP)in compressive sensing theory: • ensure the low dimensional features preserve the intrinsic structure of the high-dimensional features; • Very sparse: For computational efficiency;

  14. Compressive Tracking Algorithm (con’t) • Appearance model using the following feature vector f

  15. Compressive Tracking Algorithm (con’t) • Classifier and updating • Use Naïve Bayesian classifier • Online update scheme for parameters in the classifier

  16. Outline • Paper & Author information • Online Visual tracking Introduction • Compressive Tracking Algorithm • Experimental results • Questions

  17. Experimental results 实验视频段来自如下几个数据集： • MIL data set • VTD data set • L1 data set

  18. Experimental results（con’t） Experimental results（con’t） • David：能正确跟踪，仅有少许偏差，但不影响跟踪结果 • David：能正确跟踪，仅有少许偏差，但不影响跟踪结果 #1 #238 #258 #276 #303 #319

  19. Experimental results（con’t） • Bolt：无背景干扰正确跟踪，反之，完全跟丢 #1 #85 #108 #126 #149 #136 #153 #161

  20. Experimental results（con’t） • Biker：能正确跟踪，但跟踪窗口大小固定，不能处理放大情况 #43 #1 #90

  21. Experimental results（con’t） Experimental results（con’t） • kitesurf：未发生显著形变前，能跟踪，之后，不能跟踪到目标 • kitesurf：未发生显著形变前，能跟踪，之后，不能跟踪到目标 #1 #23 #25 #34 #30 #31

  22. Experimental results（con’t） Experimental results（con’t） • animal：基本跟踪不到目标 • animal：基本跟踪不到目标 #1 #4 #12

  23. Experimental results（con’t） • football：目标中度遮挡或完全遮挡时，跟丢目标 #1 #44 #54 #62 #73 #82

  24. Outline • Paper & Author information • Online Visual tracking Introduction • Compressive Tracking Algorithm • Experimental results • Questions

  25. Questions • 图像多尺度表示： 其物理意义是什么？有无必要这样表示？ • 投影随机性问题 如何保证跟踪的稳定性？【演示Biker和其他视频段】 • 压缩感知问题 这里没有涉及到压缩感知理论；仅是基于随机矩阵投影

  26. Questions（con’t） • David：均匀分割目标区域方法与多尺度方法比较，跟踪效果没多大差别，其中黄色为原始方法；其他很多视频情况类似，【运行该跟踪视频】 #1 #4 #94 #115 #120 #274

  27. Questions（con’t） • Dollar、football：MSER与多尺度方法比较，跟踪稳定性获得稍许改善，【演示该效果】。

  28. 实验结果总结 • 算法运算速度快，能实时跟踪 • 每个视频都有可能出现最好结果，但不是每次都重现； • 大量视频存在定位不准的情况； • 尺度缩放问题未能解决； • 当前算法跟踪错误后无法纠正； • 剧烈运动视频大概率跟丢； • 遮挡问题未能解决； • 光照显著变化时大概率跟丢；

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