A PRESENTATION OFA REAL-TIME ROBUST OBJECTTRACKINGPrarinya Siritanawan (SIIT)Toshiaki Kondo (SIIT), Kanokvate Tungpimolrut...
OUTLINE• Introduction• Hamming Distance based Gradient  Orientation Pattern Matching• Experimental Results• Conclusion• Qu...
INTRODUCTION“OBJECT TRACKING IS A DETERMINATION OF LOCATION,    PATH AND CHARACTERISTICS OF AN INTERESTED OBJECT”      Sub...
INTRODUCTIONApplications for object tracking• Video surveillance• Human-machine interface• Robot control• Air space monito...
INTRODUCTIONMajor problems of visual tracking are caused by• Illumination change• Occlusion                  We focus on t...
INTRODUCTION• Typical visual tracking and motion estimation  techniques assume that lighting conditions are  constant and ...
INTRODUCTION • Template matching is the intensity-based technique   for measuring the similarity between template and   co...
INTRODUCTION• We obtain an array of SADs or SSDs after scanning  the template over the entire image.   Best matching   pos...
INTRODUCTION• However SADs and SSD are sensitive to changing  lighting conditions and occlusion. In order to  develop a me...
HAMMING DISTANCED BASED GRADIENT    ORIENTATION PATTERN MATCHING                                   10
HAMMING DISTANCE BASED GRADIENTORIENTATION PATTERN MATCHING• Hamming distance based Gradient Orientation  Pattern Matching...
Sample Image                   Template                               1st Derivative (Sobel Operator)      Step 1         ...
UNIT GRADIENT VECTORS  • UGV is a robust feature against Illumination changes.Normalcondition            Intensity image  ...
UNIT GRADIENT VECTORS• The unit gradient vectors (UGVs) feature can be  extracted through the following normalized  equati...
MATCHING METHOD• We introduce Hamming Distance (HD),                                                    0   0   1   1     ...
MATCHING METHOD• HD uses XOR but UGVs is not binary info.• We need to transform the non-binary image  to be binary image u...
MATCHING METHOD• Then the total distance of the block at position  (x,y) is given by Fig. 4. Inverted HD        result.   ...
MATCHING METHOD  Image       Template        Occluded          Similarity       Score features                      Image ...
EXPERIMENTAL RESULTS                       19
DEMONSTRATION                20
EXPERIMENTAL RESULTSWhich is the                        ?                         ?                                       ...
CONCLUSION• A novel pattern matching technique combines  the advantages of  – Unit gradient vectors (UGVs)  – Hamming dist...
PUBLICATION1.   Wattanit Hotrakool, Prarinya Siritanawan, and Toshiaki Kondo, “Real-time     Gradient Orientation Pattern ...
ACKNOWLEDGEMENTThis research is supported by• National Research University Project of Thailand,  Office of Higher Educatio...
THANK YOU FOR YOUR ATTENTIONQUESTION AND ANSWER                               25
Upcoming SlideShare
Loading in...5
×

Kccsi 2012 a real-time robust object tracking-v2

1,182

Published on

Presentation at International Advanced School on Knowledge Co-creation and Service Innovation 2012, Japan Advanced Institute of Science and Technology, March 1

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,182
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
18
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Kccsi 2012 a real-time robust object tracking-v2

  1. 1. A PRESENTATION OFA REAL-TIME ROBUST OBJECTTRACKINGPrarinya Siritanawan (SIIT)Toshiaki Kondo (SIIT), Kanokvate Tungpimolrut (NECTEC), Itsuo Kumazawa (Tokyo Tech)Master of Information and Communication Technology for Embedded SystemSirindhorn International Institute of Technology Presentation at International Advanced School on Knowledge Co-creation and Service Innovation 2012, Japan Advanced Institute of Science and Technology, March 1 1
  2. 2. OUTLINE• Introduction• Hamming Distance based Gradient Orientation Pattern Matching• Experimental Results• Conclusion• Question and Answer 2
  3. 3. INTRODUCTION“OBJECT TRACKING IS A DETERMINATION OF LOCATION, PATH AND CHARACTERISTICS OF AN INTERESTED OBJECT” Subhash Challa, Mark R. Morelande, Darko Musichki and Robin J. Evans, “Fundamentals of Object Tracking”, Cambridge University Press, 2011
  4. 4. INTRODUCTIONApplications for object tracking• Video surveillance• Human-machine interface• Robot control• Air space monitoring• Weather monitoring• Cell biology Subhash Challa, Mark R. Morelande, Darko Musichki and Robin J. Evans, ”Fundamentals of Object Tracking”, Cambridge University Press, 2011 4
  5. 5. INTRODUCTIONMajor problems of visual tracking are caused by• Illumination change• Occlusion We focus on these problems• Computation time• Scaling• Rotation• Focus• Aperture 5
  6. 6. INTRODUCTION• Typical visual tracking and motion estimation techniques assume that lighting conditions are constant and minimal occlusion.• We proposed a new template matching technique. 6
  7. 7. INTRODUCTION • Template matching is the intensity-based technique for measuring the similarity between template and corresponding block of image. x Popular similarity metrics SAD I (i , j ) T (i , j )Template y i j 2 SSD I (i , j ) T (i , j ) i j Match 7 Sample frame
  8. 8. INTRODUCTION• We obtain an array of SADs or SSDs after scanning the template over the entire image. Best matching position Fig. 1. Inverted SSD result. 8
  9. 9. INTRODUCTION• However SADs and SSD are sensitive to changing lighting conditions and occlusion. In order to develop a method that can provide the robustness to illumination change, a new template matching technique is used.• For illumination change problem, we introduced a robust feature called Unit gradient vector (UGVs).• To cope with the occlusion problem, we introduce the Hamming distance as a new matching method instead of SSD. 9
  10. 10. HAMMING DISTANCED BASED GRADIENT ORIENTATION PATTERN MATCHING 10
  11. 11. HAMMING DISTANCE BASED GRADIENTORIENTATION PATTERN MATCHING• Hamming distance based Gradient Orientation Pattern Matching (P.Siritanawan & T.Kondo) – Template matching based technique using Hamming distance (HD) on Unit gradient vectors (UGVs). Fig. 2. Intensity image. Fig. 3. Unit gradient vectors in x and y direction 11
  12. 12. Sample Image Template 1st Derivative (Sobel Operator) Step 1 gx1 gy1 gx2 gy2 Extract UGVs feature Normalize nx1 ny1 nx2 ny2 Threshold Threshold Step 2 Absolute Diff. Absolute Diff.Perform templatematching by using ORHamming distance Block at position Sum (x,y) Iterate (N-M-1) blocks return [Best matching position (x,y)] 12
  13. 13. UNIT GRADIENT VECTORS • UGV is a robust feature against Illumination changes.Normalcondition Intensity image Gradient vectors Unit gradient vectorsLightingchangecondition 13
  14. 14. UNIT GRADIENT VECTORS• The unit gradient vectors (UGVs) feature can be extracted through the following normalized equations where Ix and Iy are gradient of intensities in x and y direction is a small constant to prevent zero division 14
  15. 15. MATCHING METHOD• We introduce Hamming Distance (HD), 0 0 1 1 0 0 1 1 = 0 0 1 1 0 0 0 1 1st Pattern 2nd Pattern HD(x,y) = 8 HD counts the number of pixels that are not match 15
  16. 16. MATCHING METHOD• HD uses XOR but UGVs is not binary info.• We need to transform the non-binary image to be binary image using threshold absolute difference function (O.Pele & M.Werman), 16
  17. 17. MATCHING METHOD• Then the total distance of the block at position (x,y) is given by Fig. 4. Inverted HD result. 17
  18. 18. MATCHING METHOD Image Template Occluded Similarity Score features Image metric 1 4 2 6 1 4 0 0 0 0 2 6 SAD 6 3 4 4 6 3 0 0 0 0 4 4Intensities = 32 5 6 6 2 5 6 0 0 0 0 6 2 2 3 5 3 2 3 0 0 0 0 5 3Unit 0 0 1 1gradient 0 0 1 1 HD = 8vectors 0 0 1 1(UGV) 0 0 1 1 Pixelwise voting 18
  19. 19. EXPERIMENTAL RESULTS 19
  20. 20. DEMONSTRATION 20
  21. 21. EXPERIMENTAL RESULTSWhich is the ? ? Best matchingbest matching ?position ? ? peak found !! Fig. 5. Tracking results under irregular lighting with occlusion by (a) SSD on UGVs, (b) HD on UGVs, (c) and (d) are the distributions of the corresponding similarity measurements 21
  22. 22. CONCLUSION• A novel pattern matching technique combines the advantages of – Unit gradient vectors (UGVs) – Hamming distance metric (HD)• UGV is a robust feature against the time-varying lighting conditions.• Compared with conventional matching with SAD or SSD on intensity, HD yields better results in partial occlusion scenarios. (60-70% covered).• Efficient over the existing matching techniques on both synthetic and real image sequences. 22
  23. 23. PUBLICATION1. Wattanit Hotrakool, Prarinya Siritanawan, and Toshiaki Kondo, “Real-time Gradient Orientation Pattern Matching”, International Conference on Embedded System and Information Technology, Chaing Mai, Thailand, 20102. Wattanit Hotrakool, Prarinya Siritanawan, and Toshiaki Kondo, “A Real-time Eye- tracking Method using Time-varying Gradient Orientation Patterns”, In proc. ECTI-CON, Thailand, 20103. Prarinya Siritanawan and Toshiaki Kondo, “Hamming Distance based Gradient Orientation Pattern Matching”, In proc. International Symposium of Artificial life and Robotics 17th, Chaing Mai, Oita, Japan, January 20124. Prarinya Siritanawan, Toshiaki Kondo, Kanokvate Tungpimolrut, Itsuo Kumazawa, “A visual tracking method using the Hamming distance”, In proc. International Conference on Information and Communication Technology for Embedded System 3rd, Bangkok, Thailand, March 2012 23
  24. 24. ACKNOWLEDGEMENTThis research is supported by• National Research University Project of Thailand, Office of Higher Education Commission• Sirindhorn International Institute of Technology (SIIT)• Thailand Advanced Institute of Science and Technology (TAIST)• Tokyo Institute of Technology• National Electronics and Computer Technology Center (NECTEC) 24
  25. 25. THANK YOU FOR YOUR ATTENTIONQUESTION AND ANSWER 25
  1. Gostou de algum slide específico?

    Recortar slides é uma maneira fácil de colecionar informações para acessar mais tarde.

×