Particle Dynamics and Multi- Channel Feature Dictionaries for Robust Visual Tracking Srikrishna...
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Transcript of Particle Dynamics and Multi- Channel Feature Dictionaries for Robust Visual Tracking Srikrishna...
Particle Dynamics and Multi-Channel Feature Dictionaries for Robust Visual Tracking
Srikrishna Karanam, Yang Li, Rich Radke Dept. of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy NY
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Compressive sensing tracking
Feature dictiona
ry𝐴=[𝑡1𝑡 2⋯ 𝑡𝑛]
X. Mei and H. Ling, Robust visual tracking using minimization, ICCV 2009.
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Compressive sensing tracking
Current
state
𝑠𝑡+1=𝑠𝑡+𝒩 (0,1 )𝑢0𝑢0=𝑑𝑖𝑎𝑔(𝜎0)𝑝 (𝑠𝑡+1∨𝑠𝑡)
Hypotheses
X. Mei and H. Ling, Robust visual tracking using minimization, ICCV 2009.
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Compressive sensing tracking
Hypothesis Testing
⋯
𝑦=𝐴𝑥+𝑒 Sparse x, e
X. Mei and H. Ling, Robust visual tracking using minimization, ICCV 2009.
Contributions
APPEARANCE MODEL• Multi-channel feature
dictionaries• Image intensity• Image gradient
magnitude• Histograms of
Oriented Gradients
HYPOTHESIS GENERATION• Particle filter• Adaptive variance
Gaussian State Transition Model
HYPOTHESIS TESTING• minimization• Probabilistic reasoning• Adaptive filtering• Dictionary update
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Appearance model Intensity
Normalized gradient magnitude
Histograms of Oriented Gradients
⋯
⋯
⋯
Norm.Gradient
HOG
∑
(∶)Intensity
∑
(∶)
∑
(∶)
J. Wright and Y. Ma, Dense error correction via minimization, IEEE Trans. on Info. Theory, 2009
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Hypothesis generation – Transition model
• Contribution – Dynamic state transition model
• - state with highest observation probability
• - estimated using past states
⋮
Past statevectors
�̂� (𝒕)
𝒆 (𝒕)
𝒔 (𝒕−𝒏)
𝒔 (𝒕−𝒏+𝟏)
𝒔 (𝒕−𝟏)𝝈𝒕+𝟏
X. Mei and H. Ling, Robust visual tracking using minimization, ICCV 2009.C. Bao et al., Real-time robust tracker using accelerated proximal gradient approach, CVPR 2012.Z. Hong et al., Tracking via robust multi-task multi-view joint sparse representation, ICCV 2013.
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Hypothesis generation – Transition model• Contribution – Dynamic
state transition model
• - state with highest observation probability
• - estimated using dynamics of past states
• to be computed• Hankel matrix• Least squares minimization
𝑠𝑡+1=𝑠𝑡+𝒩 (0,1 )𝜎𝒕+𝟏
𝜎 𝑡+1=max (min (𝜎0√𝑒𝑡 ,𝜎𝑚𝑎𝑥 ) ,𝜎𝑚𝑖𝑛)
𝑒𝑡=∑𝑗=1
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𝑒𝑡𝑗𝑒𝑡
𝑗=∥ 𝑦~𝑠𝑡𝑗 − 𝑦 𝑠𝑡
𝑗 ∥2
𝑠𝑡=𝑐1𝑠𝑡−1+𝑐2𝑠𝑡− 2+⋯+𝑐𝑛𝑠𝑡−𝑛
𝐻 𝑠𝑡 −𝑛−1 ,𝑛𝑐𝑇= [𝑠𝑛+1𝑠𝑛+2⋯𝑠𝑛+ (𝑡−𝑛−1 ) ]𝑇
M. Ayazoglu et al., Dynamic subspace-based coordinated multicamera tracking, ICCV 2011.
(1)
(2)
(3)
(4)
(5)
(6)
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Hypothesis generation – Particle filtering
𝑛=𝜒 𝑘− 1,1−𝛿2
2𝜖
• Related approaches – (400-600, fixed)• Dynamic model + adaptive candidate
filtering
D. Fox, KLD-Sampling: Adaptive Particle Filters, NIPS 2001.
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Hypothesis testing min𝑥 ,𝑒
∥ 𝑥∥1+∥𝑒 ∥1𝑠 . 𝑡 . 𝑦=𝐴𝑥+𝑒
min𝑥 ,𝑒
𝐿 (𝑥 ,𝑒 ,𝑝 )
𝑥𝑖+1=argmin𝑥𝐿(𝑥 ,𝑒𝑖 ,𝑝𝑖)
𝑒𝑖+ 1=argmin𝑒𝐿(𝑥 𝑖+1 ,𝑒 ,𝑝𝑖)
𝑝𝑖+1=𝑝𝑖+𝑘(𝑦−𝐴𝑥 𝑖+ 1−𝑒𝑖+1)
FISTA
Analytic
Hypothesis • Intensity
• Norm. gradient • HOG
min in each channel
Highest observation probability
𝑝 (𝑦𝑡|𝑠𝑡 )=exp (−∑𝑗=1
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𝛼 𝑗∥ 𝐴𝑗𝑥 𝑗− 𝑦𝑡
𝑗∥22)
𝑦 𝑗
𝑥 𝑗 ,𝑒 𝑗
⋯
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Data
• Publicly available standard test sequences
Focal Length
Y. Wu et al., Online object tracking: a benchmark, CVPR 2013.
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Evaluation metrics
• Success plot
• Robustness tests• Temporal robustness test
• Spatial Robustness test
Principal Point Focal Length
• Overlap precision vs. Overlap threshold
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Experimental Results – Overall Success Plot
• Ideally, close to 1
Principal Point Focal Length
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Experimental Results – Robustness tests
• Temporal robustness evaluation
• Spatial robustness evaluation
Principal Point Focal Length
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Experimental Results
• Validating key components.
• Choice of features.
• Choice of transition model.
• Adaptive candidate filtering
Principal Point Focal Length
Distortion Coefficient
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SpeedMethod Speed
(fps)Template size
Average
distance
precision
Average AUC
Ours* 2.5 64 x 64 0.92 0.69
L1* 8.2 12 x 15 0.47 0.36
MTT* 0.4 32 x 32 0.60 0.42
ONDL* 0.5 32 x 32 0.79 0.59
SCM* 0.05 32 x 32 0.72 0.59
ASLA* 0.7 32 x 32 0.73 0.59
LSH 7 - 0.70 0.57
LOT 0.2 - 0.53 0.31
SPT 0.1 - 0.49 0.29
MIL 8.5 - 0.56 0.45
IVT 6.5 - 0.61 0.46
* - based on sparse visual representation.
This material is based upon work supported by the U.S. Department of Homeland Security, Science and Technology Directorate, Office of University Programs, under Award 2013-ST-061-ED0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security.
Conclusions• Multi-Channel features• Particle dynamical information• Adaptive filtering
Thank you! Questions?