1. In Defense of Color-based Model-free Tracking
Mooyeol Baek
Horst Possegger, Thomas Mauthner, and Horst Bischof. "In Defense of Color-based Model-free Tracking." CVPR 2015

2. Visual tracking
DATs
ACT
DSST
KCF
CV lab. seminar

3. Contributions
Achieves state-of-the-art performance on a variety of challenging sequences relying on only standard color histograms.
Extends tracker to identify and suppress distracting regions in advance which significantly improves the tracking robustness.
Proposes an efficient scale estimation scheme which allows us to obtain accurate tracking results.
CV lab. seminar

4. Overall procedure Object model update Object model update
Localization
Localization
Localization
Scale estimation
Scale estimation
Scale estimation
Tracking result
Tracking result
Tracking result
CV lab. seminar

5. Distractor-aware object model
Object-surrounding model
𝑃 𝐱∈𝐎 𝑂,𝑆, 𝑏 𝐱 ≈ 𝑃 𝑏 𝐱 𝐱∈𝑂 𝑃 𝐱∈𝑂 Ω∈ 𝑂,𝑆 𝑃 𝑏 𝐱 𝐱∈Ω 𝑃 𝐱∈Ω
= 𝐻 𝑂 𝐼 ( 𝑏 𝐱 ) 𝐻 𝑂 𝐼 𝑏 𝐱 + 𝐻 𝑆 𝐼 ( 𝑏 𝐱 )
Distractor-aware model
𝑃 𝐱∈𝐎 𝑂,𝐷, 𝑏 𝐱 ≈ 𝑃 𝑏 𝐱 𝐱∈𝑂 𝑃 𝐱∈𝑂 Ω∈ 𝑂,𝐷 𝑃 𝑏 𝐱 𝐱∈Ω 𝑃 𝐱∈Ω
= 𝐻 𝑂 𝐼 ( 𝑏 𝐱 ) 𝐻 𝑂 𝐼 𝑏 𝐱 + 𝐻 𝐷 𝐼 ( 𝑏 𝐱 )
Combined model
𝑃 𝐱∈𝐎 𝑏 𝐱 = 𝜆 𝑝 𝑃 𝐱∈𝐎 𝑂,𝐷, 𝑏 𝐱 (1− 𝜆 𝑝 )𝑃 𝐱∈𝐎 𝑂,𝐷, 𝑏 𝐱
𝑃 1:𝑡 𝐱∈𝐎 𝑏 𝐱 =𝜂𝑃 𝐱∈𝐎 𝑏 𝐱
+(1−𝜂) 𝑃 1:𝑡−1 𝐱∈𝐎 𝑏 𝐱
CV lab. seminar

6. Localization Localization 𝑂 𝑡 ∗ = arg max 𝑠 𝑣 𝑂 𝑡,𝑖 ∙ 𝑠 𝑑 𝑂 𝑡,𝑖 ,
𝑠 𝑣 𝑂 𝑡,𝑖 = 𝐱∈ 𝑂 𝑡,𝑖 𝑃 1:𝑡−1 𝐱∈𝑂 𝑏 𝐱 ,
𝑠 𝑑 𝑂 𝑡,𝑖 = 𝐱∈ 𝑂 𝑡,𝑖 exp − 𝐱− 𝐜 𝑡− 𝜎 2 ,
𝑂 𝑡,𝑖 be a distractor if 𝑠 𝑣 𝑂 𝑡,𝑖 ≥ 𝜆 𝑣 𝑠 𝑣 𝑂 𝑡 ∗
CV lab. seminar

7. Scale estimation Find adaptive threshold 𝜏 ∗ Thresholding BB Update
𝜏 ∗ = arg min 𝑐 𝑂 𝐿 𝑏 𝜏 + 𝑐 𝑆 𝐿 𝑏 𝜏 − 𝑐 𝑂 𝐿 𝑏 𝜏 +1 − 𝑐 𝑂 𝐿 𝑏 𝜏 ,
𝑠.𝑡 𝑐 𝑂 𝐿 𝑏 𝜏 + 𝑐 𝑆 𝐿 𝑏 𝜏 ≥1.
Thresholding
BB Update
Model update
𝑂 𝑡 = 𝜆 𝑆 𝑂 𝑡 𝑆
+ 1− 𝜆 𝑆 𝑂 𝑡 ∗
CV lab. seminar

8. Evaluation Performance scores and ranking results 1st 2nd 3rd
CV lab. seminar

9. Evaluation Performance scores and ranking results 2015-07-13
CV lab. seminar

10. Thank you!
CV lab. seminar