Moving Cast Shadow Detection using Physics-based Features
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Moving Cast Shadow Detection Using Physics-based Features (CVPR 2009)


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Moving Cast Shadow Detection Using Physics-based Features (CVPR 2009)

  1. 1. Moving Cast Shadow Detection using Physics-based Features Jia-Bin Huang and Chu-Song Chen Institute of Information Science, Academia Sinica, Taipei, Taiwan E-Mail:, Goal Physics-Based Shadow Model Results • Detecting moving objects in a scene • Bi-illuminant Dichromatic Reflection Model • Qualitative Results Decompose reflected light into four types: body and surface reflection for direct light sources and ambient illumination (Maxwell et al. CVPR 2008) I (λ) = cb (λ)[mb ld (λ) + Mab (λ)] + cs (λ)[ms ld (λ) + Mas (λ)]. Consider only matte surfaces, the camera sensor response i i i i gi = αFi mb cb ld + Fi cb Mab , i ∈ {R , G , B } defines a line segment in the RGB color space: shadowed pixel to fully lit pixel. • Extracting Useful Features Spectral ratio Input: Video sequence Output: Label fields SD i α i Mab Si = i − SD i = + i BG 1 − α (1 − α)mb ld Subtract the bias term Challenges γi = Si − α = Mabi 1−α i (1 − α)mb ld Normalized spectral ratio • Shadows may be misclassified as foreground because: i Mab 1 • 1) they are typically significantly different from background γi = ˆ i |γ| , (1 − α)mb ld • 2) they have the same motion as foreground objects (a) (b) (c) (d) (e) R G B 1 Mab 2 Mab 2 Mab 2 • 3) they are usually attached to foreground objects where |γ| = (1−α)mb ( lR ) + ( lG ) + ( lB ) Figure: (a) Input frame. (b) Background posterior probability P (BG |xp ). (c) Confidence d d d map by the global shadow model. (d) The shadow posterior probability P (SD |xp ). (e) The foreground posterior probability P (FG |xp ). Related Works • Quantitative Results Learning Cast Shadows Evaluation metrics: shadow detection and discrimination rate: TPS TPF • Shadow detection with static parameter settings η= ;ξ = TPS + FNS TPF + FNF Require significant parameter tuning • Weak Shadow Detector Cannot adapt to environment changes Table: Quantitative results on surveillance sequences • Shadow detection using statistical learning methods Sequence Highway I Highway II Hallway [1] Shadow flow (Porikli et al., ICCV 2005) Evaluate every moving pixels detected by the Methods η % ξ% η % ξ% η % ξ% [2] Gaussian mixture shadow model (Martel-Brisson et al., CVPR 2005) background model to filter out impossible ones Proposed 70.83 82.37 76.50 74.51 82.05 90.47 [3] Local and global features (Liu et al., CVPR 2007) Kernel [4] 70.50 84.40 68.40 71.20 72.40 86.70 [4] Physical model of cast shadows (Martel-Brisson et al., CVPR 2008) LGf [3] 72.10 79.70 - - - - • Major Drawback: GMSM [2] 63.30 71.30 58.51 44.40 60.50 87.00 All are pixel-based methods: need numerous foreground activities to learn • Global and Local Shadow Model Handling Scene with Few Effect of Confidence-Rated Gaussian the parameters. Learn a global shadow model for the whole scene using Gaussian Mixture Model Mixture Learning Foreground Activities (GMM) with the Expectation-Maximization (EM) algorithm: K p (ˆ|µ, Σ) = r πk Gk (ˆ, µk , Σk ) r Contributions k =1 Cast shadows are expected to form a compact cluster (with large mixing weight and small variance) •A global shadow model learned from physics-based features. Build pixel-based GMM to learn local features (gradient density distortion) • Does not require numerous foreground activities or high frame • Confidence-Rated Gaussian Mixture Learning rates to learn the shadow model parameters Adaptive learning rate for mixing weights and Gaussian parameters: (a) (b) (c) • Can be used for fast learning of local features in pixel-based Figure: (a)Background image. (b) The Figure: Quantitative results of the 1 − ρdefault models ρπ = C (ˆ) ∗ ( γ ) + ρdefault mean map with confidence-rated learning. Intelligent Room sequence under K j =1 cj (c) The mean map w/o using different frame rates settings (10, 5, 1 − ρdefault confidence-rated learning. 3.33, and 2.5 frame/sec) ρG = C (ˆ) ∗ ( γ ) + ρdefault ck Jia-Bin Huang and Chu-Song Chen (IIS, Academia Sinica, Taiwan) IEEE Conference on Computer Vision and Pattern Recognition 2009 (CVPR 2009)