People Detection in Video Stream

1. Cairo University Faculty of Engineering Computer Engineering Department People Detection in Video Stream PresentedBy: Engy Foda Supervised By: Dr. Ahmed Darwish Dr. Ihab Talkhan Dr. Salah El Tawil

2. Contents • Problem Definition • Motivation • Literature Survey • Art Theories • Artistic People Detection System • Experimental Results in Images • Experimental Results in Video • Future work

3. Problem Definition

4. Motivation • It is needed by many applications; multimedia applications, traffic control, humanoids and robotics, intelligent cars embedded systems, security.

5. Challenges • Edge detection, color detectors techniques. • It is hard to model as it is non-rigid object.

6. Literature survey • People detection in still images • People detection in video • Wavelets and Haar Transform • Detection by components • Dynamic detection information • Tracking • 3D modeling • Kalman filter

7. Art Theories • Vitruvian Man by Ancient Roman architect Vitruvius • Vitruvian Man by Leonardo Da Vinci • Human Body Proportions Standards Theory • Proportions used in our system

8. Human Body Proportions Standards Theory • The human body is -in average- of 7 heads high. • Shoulder to shoulder width is 3 heads. • Hip to toes height is 4 heads. • Top of the head to the bottom of the chest is 2 heads high. • Wrist to the end of the outstretched fingers of the hand is 1 head in length. • Top to bottom of the buttocks is 1 head in length. • Elbow to the end of outstretched fingers is 2 heads in length.

9. Proportions used in the system

10. Artistic People Detection System • Skin Detection • Face Detection • Human Body Detection

11. Detection Technique DISCARDED DISCARDED • Detect probable skin regions from the image. • Discard skin regions of area <3% of the whole image area. • Template resize and orientation. • Perform cross correlation. • Apply body proportions and mark body components.

12. Video Detection Technique • Break the video into successive frames . • Apply the whole image detection technique on each frame. • Assemble the detected frames in a new video file showing the detected persons.

13. Contributions • Human Body detection based on artistic theory. • Selecting the appropriate proportions from the standard theory. • Using the skin detection and face detection as phases for body detection. • Experimental values of cross correlation [0.5, 0.7].

14. Advantages • Ability to detect partial bodies. • Detect human body by components. • Does not require fixed setup. • Simple Processing.

15. Limitations The following cases are not resolved by this system: • Covered faces. • Body is in up side down position. • Pygmies. • Faces with sun glasses, beards, hats. (resolved with low accuracy) • Filtering the regions by area experimentally by <3%.

16. Experimental Results in Images

17. Experimental Results in Images

18. Experimental Results in Images

19. Experimental Results in Images

20. Experimental Results in video

21. Experimental Results in video

22. Samples of results in images Whole Body without background • Correct: • Exact 3 parts • Whole body • 2 parts • False • Fail: • Background • Not Detected • Wrong

23. Results of Video Part

24. Future Work • Modifications on image processing part. • Modifications on video processing part.

25. Modifications on Image Part • Boundary or contour detection for the human body. • More body components, e.g. every arm, every leg. • Neural networks to learn the human body architecture.

26. Modifications on Video Part • More processing to the dynamic information of the video sequence.

27. Thank You efoda@ieee.org

28. Exact 3 parts

29. Whole Body

30. 2 Parts

31. False Detection

32. Background

33. Not Detected

34. Wrong Detection

35. More Detailed Statistics