kaihua zhang lei zhang (polyu, hong kong) ming-hsuan yang (uc merced, california, u.s.a. ) real-time...
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Kaihua Zhang
Lei Zhang
(PolyU, Hong Kong)
Ming-Hsuan Yang
(UC Merced, California, U.S.A. )
Real-Time Compressive Tracking
European Conference on Computer Vision, 2012 (ECCV 2012)
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Outline
• Introduction
•Random projection
•Classifier construction and update
• Experiments
•Conclusion
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Outline
• Introduction
•Random projection
•Classifier construction and update
• Experiments
•Conclusion
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Introduction
• Propose an effective and efficient tracking algorithm with an appearance model based on features extracted in the compressed domain.
• Our appearance model employs non-adaptive random projections that preserve the structure of the image feature space of objects
• Compress samples of foreground targets and the background using the same sparse measurement matrix
• The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain
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Compressed sensing
• Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems (Emmanuel Candès, Terence Tao, and David Donoho)
• There are two conditions under which recovery is possible
• The first one is sparsity which requires the signal to be sparse in some domain
• The second one is incoherence which is applied through the restricted isometry property (RIP) which is sufficient for sparse signals.
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Random Measurement Matrix
high-dimensional space
lower-dimensional space
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Main components of compressive tracking
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Frame(t) Samples
Classifier
Sparse projection
matrix
Compressed vectors
Image features
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Main components of compressive tracking(2)
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Sample with maximal classifier response
Frame(t+1)
Classifier
Frame(t+1)Sparse measurement
matrix
Multiscale Image features
Compressed vectors
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Outline
• Introduction
•Random projection
•Classifier construction and update
• Experiments
•Conclusion
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Random projection
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A matrix R whose rows have unit length projects data from the high-dimensional image space x to a lower-dimensional space v
v = Rx where nm
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Random projection(2)
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Intuitive interpretation of JL lemma linear map f: ->
𝑹𝒏
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Random projection(3)
• We write the Euclidean distance between two data vectors and in the original large-dimensional space as
• After the random projection, approximated by the scaled Euclidean distance of these vectors in the reduced space:
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where d is the original and k the reduced dimensionality of the data set
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Random projection(4)
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(Johnson-Lindenstrauss lemma)
Given 0<<1, a set X of d points in and number n > 8ln(m)/,there is a linear map f: -> such that
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The Johnson-Lindenstrauss Lemma
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• Selecting matrix A that provide the desired result are:
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Random Measurement Matrix
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𝑹𝒎
𝑹𝒏
Construct random matrix R such that JL lemma
We adopt a very sparse random matrix with entries defined as
This type of matrix with s =3 satisfies the JL-lemma by Achlioptas
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Random Measurement Matrix(2)
• In this work, we set s = m/4 which makes a very sparse random matrix
• For each row of R, only about c, c ≤ 4, entries need to be computed
• Therefore, the computational complexity is only O(cn) which is very low
• Furthermore, we only need to store the nonzero entries of R which makes the memory requirement also very light.
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Multiscale filter
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Haar-like features
• Haar-like features are digital image features used in object recognition
• A simple rectangular Haar-like feature can be defined as the difference of the sum of pixels of areas inside the rectangle
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Dimensionality Reduction
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Graphical representation of compressing a high-dimensional vector x to a low-dimensional vector v x х = 𝑣 𝑖=∑
𝑗
𝑟 𝑖𝑗 𝑥 𝑗
Negative entry Positive entry
Zero entry
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Analysis of Low-Dimensional Compressive Features
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Outline
• Introduction
•Random projection
•Classifier construction and update
• Experiments
•Conclusion
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Classifier Construction
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For each sample z its low-dimensional representaion
Assume all elements in v are independently distributed model them with a naïve Bayes classifier
=
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Classifier Update
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Contitional distributions and in the classifier H(v) are assumed to Gaussian distributed with four parameters
) )
The scalar parameters are incrementally updated:
+
where
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Algorithm : Compressive Tracking
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Outline
• Introduction
•Random projection
•Classifier construction and update
•Experiments
•Conclusion
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Experiments
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http://www4.comp.polyu.edu.hk/~cslzhang/CT/CT.htm
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Experiments(2)
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Experiments(3)
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Outline
• Introduction
•Random projection
•Classifier construction and update
• Experiments
•Conclusion
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![Page 29: Kaihua Zhang Lei Zhang (PolyU, Hong Kong) Ming-Hsuan Yang (UC Merced, California, U.S.A. ) Real-Time Compressive Tracking](https://reader036.vdocument.in/reader036/viewer/2022062422/56649eb15503460f94bb68f0/html5/thumbnails/29.jpg)
Conclusion
• In this paper, we proposed a simple yet robust tracking algorithm with an ap- pearance model based on non-adaptive random projections that preserve the structure of original image space
• Numerous experiments with state-of-the-art algorithms on challenging sequences demonstrated that the proposed algorithm performs well in terms of accuracy, robustness, and speed.
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