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Performance Evaluation of Shadow Detection Algorithms

Performance Evaluation of Shadow Detection Algorithms. Like Zhang University of Texas at San Antonio Heng-Ming Tai University of Tulsa, Tulsa, OK Chwan-Hwa Wu * Auburn University . Introduction.

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Performance Evaluation of Shadow Detection Algorithms

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  1. Performance Evaluation of Shadow Detection Algorithms Like Zhang University of Texas at San Antonio Heng-Ming Tai University of Tulsa, Tulsa, OK Chwan-Hwa Wu* Auburn University

  2. Introduction • This paper conducts the performance evaluation for several shadow detection algorithms based on different color spaces. • Background model in the object segmentation algorithm is updated using information from pixel changes and the detected object. • Performance comparison among several shadow detection schemes in strong light and weak light conditions is presented.

  3. Object Segmentation Problems • Light changes • Background changes (car moves in/out) • Shadow effects

  4. Pixel-based Segmentation Frame n Frame n-1 Background Update Frame Difference Background Difference Object Extraction VOP Post Processing Video Object

  5. Object-based Adaptive Background Update Frame n Frame n-1 Frame Difference Background Update Background Difference Moving Object Still Object Object Extraction Object Classification VOP Post Processing Video Object

  6. Background Update Algorithm • Find frame difference (FD) • Get extracted object information (VO) • Identify Still object • Update background

  7. Shadow DetectionShadows in object segmentation and tracking for two video sequences: (a) Campus, (b) Parking lot

  8. Shadow Detection Methods based on color space Color spaces are characterized with different bases that represent intensity and color information in color images • YUV • HSV • RGB Method based on gradient information • Edge Difference

  9. Shadow Detection(YUV Space) • YUV has been used by most image compression standards such as JPEG, H.261, and MPEG. • YUV color space assume the following hypotheses on the environment • strong light source • static and planar background • Properties: • Insensitive to illumination changes • Performs best in weak light conditions (indoor or cloudy day)

  10. Shadow Detection(YUV Space) • Shadow pixel can be described as where k(x,y) is the reflectance of the object surface and Ek(x,y) the irradiance • If a background point is covered by the shadow, we have CA and Cp are the intensity of the ambient light and of the light source, respectively. L denotes the direction of the light source and N(x,y) the object surface normal

  11. Shadow Detection(HSV Space) • HSV describes any color in terms of three quantities - Hue, Saturation, and Value. • HSV color space corresponds closely to human perception of color • The color information improves the discrimination between shadow and object • To achieve better distinction between moving cast shadows and moving object, a shadow mask SPk for each (x,y) points is defined as

  12. Shadow Detection(Proposed Edge-based RGB Space) Proposed scheme: Edge-based gradient algorithm in RGB space • Difference of color channels • Shadow detection

  13. Shadow Detection(Edge-Difference Method ) Alternative approach for shadow removal is to use the gradient filter • Shadow area tends to have a slow gradient change in luminance value • After taking the gradient, values in the shadow region tend to be very small while the edges have large gradient values • In the indoor environment or weak light condition, the pixel value difference among the neighbors of edge points is very small. • The edge difference method to extract object and remove shadow is: ED: the object edges, In: current frame, x,y: location of current pixel

  14. Shadow Detection and Removal Highway 1 Parking Lot Monitoring

  15. Matching Error The accuracy of extracted object comparing with the standard object mask (extracted manually) Tkl : hand-drawn target mask (extracted manually) Okl : extracted object image Pixel-based Intra-frame Objectl-based

  16. VOP Extraction and Background Update Frame 65 Initial Background Frame 100 Frame 110 Updated background Extracted object

  17. Object Segmentation Pixel-based Inter-Frame Subtraction Proposed Object-based Frame 100 Frame 110 Frame 120

  18. Highway Monitoring Frame 1 Frame 110 Updated background Extracted object

  19. Strong Light Condition Object with shadow YUV HSV Edge Original RGB

  20. Matching Error (Strong Light) Solid line: RGB Dash line: Edge Dot line: HSV Dash-dot line: YUV

  21. Weak Light Condition Object with Shadow HSV YUV RGB Edge Original

  22. Matching Error (Weak Light) Solid line: RGB Dash line: Edge Dot line: HSV Dash-dot line: YUV

  23. Conclusions • Performance evaluation of different shadow detection methods has been examined in the strong light and the weak light conditions. • Related work: • Haritaoglu, et al., “W4: Real-time surveillance of people and their activities”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, pp. 809-830, Aug. 2000 • Prati, et al., “Detecting moving shadows: algorithms and evaluation”, IEEE Trans. Pattern Analysis and Machine Intelligence, 25, pp. 918-923, July 2003

  24. Thank you!Questions ? ?

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