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A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications

A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications. Lucia Maddalena and Alfredo Petrosino , Senior Member, IEEE. Adviser : Chih-Hung Lin Speaker : Kuan-Ju Chen Date : 2009/04/06. Author. Lucia Maddalena

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A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications

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  1. A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications Lucia Maddalena and Alfredo Petrosino, Senior Member, IEEE Adviser : Chih-Hung Lin Speaker : Kuan-Ju Chen Date : 2009/04/06

  2. Author • Lucia Maddalena • received the Laurea degree (cum laude) in mathematics and the Ph.D. degree in applied mathematics and computer science from the University of Naples Federico II, Naples, Italy. • Alfredo Petrosino • (SM’02) is an Associate Professor of computer science at the University of Naples Parthenope, Naples, Italy.

  3. 1 2 3 4 INTRODUCTION METHOD EXPERIMENTAL RESULTS CONCLUSION OUTLINE

  4. 1.INTRODUCTION • VISUAL surveillance is a very active research area in computer vision • The main tasks in visual surveillance systems • motion detection • object classification • Tracking • activity understanding • semantic description

  5. 1.INTRODUCTION • The usual approach to moving object detection is through background subtraction • Compared to other approaches, The main problem is its sensitivity to dynamic scene changes • light changes • moving background • cast shadows

  6. 1.INTRODUCTION • Background subtraction: • Unimodal versus multimodal: • Recursive: • Pixel-based :

  7. 1.INTRODUCTION • Unimodal and multimodal: • Basic background models assume that the intensity values of a pixel can be modeled • low complexity • cannot handle moving backgrounds

  8. 1.INTRODUCTION • Recursive • recursively update a single background model based on each input frame. • Space complexity is lower • Background model is carried out for a long time period

  9. 1.INTRODUCTION • Pixel-based : • assume that the time series of observations is independent at each pixel

  10. 1.INTRODUCTION • Our approach is based on the background model automatically generated by a self-organizing method • and can be broadly classified as multimodal, recursive, and pixelbased.

  11. 1 2 Initial Background Model Subtraction and Update of the Background Model 2.METHOD

  12. 2.1 Initial Background Model a1 a2 a3 b1 b2 b3 c1 c2 c3 a4 a5 a6 b4 b5 b6 c4 c5 c6 a b c a7 a8 a9 b7 b8 b9 c7 c8 c9 d e f d1 d2 d3 e1 e2 e3 f1 f2 f3 d4 d5 d6 e4 e5 e6 f4 f5 f6 d7 d8 d9 e7 e8 e9 f7 f8 f9 Let be HSV components , ex: a1=(h,s,v)

  13. 2.2 Subtraction of the Background Model

  14. 2.2 Subtraction of the Background Model Use Euclidean distance to compute Cn and Cotherpixel distance

  15. 2.2 Subtraction of the Background Model

  16. 2.2 Update of the Background Model • If best match cm • Weight vector At to update in the neighborhood cm • If best match cm isn`t found • Not update

  17. 2.2 Update of the Background Model a1 a2 a3 b1 b2 b3 c1 c2 c3 a4 a5 a6 b4 b5 b6 c4 c5 c6 a b c a7 a8 a9 b7 b8 b9 c7 c8 c9 d e f d1 d2 d3 e1 e2 e3 f1 f2 f3 d4 d5 d6 e4 e5 e6 f4 f5 f6 d7 d8 d9 e7 e8 e9 f7 f8 f9 If best match cm Computer the weight vector to update background

  18. 2.2 Update of the Background Model a1 a2 a3 b1 b2 b3 c1 c2 c3 a4 a5 a6 b4 b5 b6 c4 c5 c6 a7 a8 a9 b7 b8 b9 c7 c8 c9 d1 d2 d3 e1 e2 e3 f1 f2 f3 d4 d5 d6 e4 e5 e6 f4 f5 f6 d7 d8 d9 e7 e8 e9 f7 f8 f9

  19. SHADOW DETECTION Foreground Ayalyze Hue-Saturation-Value(HSV) color space Following three condition to mask shadow identifying as shadows those points define a darkening effect of shadows shadow mask: average image luminance

  20. 3.EXPERIMENTAL RESULTS (a) original frame; (b) computed moving object detection mask (c) background model (d) background model change mask from previous frame

  21. 3.EXPERIMENTAL RESULTS (a) original frame; (b) computed moving object detection mask

  22. 3.EXPERIMENTAL RESULTS

  23. 3.EXPERIMENTAL RESULTS • test image (b) ground truth • (c) SOBS result (d) Pfinder result • (e) VSAM result (f) CB result

  24. 4.CONCLUSION • This paper also includes a comprehensive accuracy testing, performed with both pixel-based and frame-based metrics • Experimental results, using different sets of data and comparing different methods, have demonstrated the effectiveness of the proposed approach • illumination changes • cast shadows • ONGOING WORK • improve detection results

  25. Thank You ! www.themegallery.com

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