1. Human Activity Recognition Based on Silhouette Directionality IEEE TRANSACTIONS ON CIRCUITS AND SYATEM FOR VEDIO TECHNOLOGY, VOL.18, NO.9, SEPTEMBER 2008 Group 15 OSLAB 陳信宏 張簡中泰

2. introduction  Dynamic interactions between machines and human  Science of detection  Tracking  Recognition of human activity  Application  Medical diagnostics  Surveillance  Smart room  Video indexing and retrieval

3. introduction  HMA (human motion analysis)  A man-machine interfacing  Determine the location of humans in a scene, and recognizes their activities  HMA methodology classification  What kind of sensor you used  How many sensor you used  Dimensionality of space (2-D or 3-D)

4. introduction  This paper present a kind of HMA  Vector space analysis of silhouette directionality for human activity recognition  用 Fg 的方向向量來判斷

5. Proposed algorithm A. background-foreground separation(1/3)  Related work:  Background subtraction  Optic flow  Temporal differencing  Staticstical modeling  Proposed one:  A statistical background model

6. Proposed algorithm A. background-foreground separation(2/3)  Build this model by computing mean and variance of intensities of each pixel over a set of of initial frames (bg only)  Ex: 先抓 100 張 bg 的 frames, 取平均  Use a threshold τ(x,y) for each pixel p(x,y)

7.  μ( x,y) : 對 bg 抓 n 張 frame 再取平均  大於 threshold 就 判定為 fg Proposed algorithm A.background-foreground separation(3/3)

8.  After step 1, each frame have 2 levels :  Fg-image ( 白 )  Bg-image( 黑 ) Proposed algorithm B.silhouette extraction and representation(1/3)

9.  Extraction example: a. Original b. Separated fg c. First erosion d. Dilation e. Second erosion f. Edge extraction a. 實作 : 上 → 下，左 → 右方向做 scan Proposed algorithm B. silhouette extraction and representation(2/3) Remove noise

10. after extracting the fg contour ? give it a representation for algo. used

11.  Silhouette representation:  We represents the fg boundary as a chain code  Assume each pixel is connected to its 8 neighbors  Generate a chain-code signature of the fg contour Proposed algorithm B.silhouette extraction and Representation (3/3)

12. ready for recognition

13. C.feature vector extraction and normalization

14.  feature vector extraction  What feature ? human activity  Normalization  Different scale (possibly due to camara position) Proposed algorithm C.feature vector extraction and normalization (1/3)

15. observation Same poses → small variance

16. observation

17. C. feature vector extraction and normalization (2/3)  Based on observations above,we have 3 assumptions:  Same activity,same distance → samll variance  Same activity,different distance → samll variance  Different activity → high variance

18. Training

19. D. Vector Space Analysis  採用內積  內積最大值為 1 ：表兩向量相似度 100 ％  在此將八個方向視為八個向量，因此將內積最 大值之 training data 動作視為影像中人物動作。  Angular Distance

20. D. Vector Space Analysis （ cont. ）

21. E.Temporal Smoothing  Poor foreground–background separation in some frames  make mistakes in recognizing  lead to large variance in the directional vector of neighboring frames

22. E.Temporal Smoothing (cont.)  First  Mark potentially erroneous frames.  Second  Compute the mean decision over a larger window size  Based on the numeric value closest to this mean we correct the activity decisions

23. E.Temporal Smoothing (cont.)  First  Mark potentially erroneous frames.  Second  Compute the mean decision over a larger window size  Based on the numeric value closest to this mean we correct the activity decisions

24. E.Temporal Smoothing (cont.)

25. Discussion  1.back-fore ground separation 時，光線問題 ，若考慮加入到判斷式裡，效果會更好（ ex:0.7*b+0.6*g+0.8*r ）？  2. 在處理每一個 pixel 時，若 BGR 分開處理（ 8bits,8bits,8bits ）則不會有同時處理（ 24bits ）的權重問題？  3. back-fore ground separation 的 threshold 使 用幾％？（似乎 33 ％效果比 100 ％好）

26. Demo Thank you group 15