1. 동영상 처리

2. 배경 모델을 이용한 이동 물체 검출

3. 차영상에 기초한 이동 물체 검출

4. 광류(Optical Flow): 카메라 이동 방향은?

5. 가정 - 물체의 위치가 바뀌어도 밝기 값은 보존된다
There are couple of schemes to detect moving object or motion itself.
Most schemes use the assumption that pixels may change their locations due to motion, but their brightness may remain unchanged.
This kind of assumption is called brightness constancy assumption.

6. 광류(optical flow)
This picture shows optical flow which denotes velocity vector of each pixel.

7. 샷 경계 검출

8. 비디오 구조

9. 비디오 구조 Frame: typically 1/25 or 1/30 seconds
Shot: sequence of similar frames
elementary video units
a single event
Clip / Scene: sequence of shots consecutive in time, space, action
Episode: consecutive scenes
intro, news, reporter, weather

10. 컷(frame between shots) 검출
샷 경계가 생기는 이유는
camera breaks (cuts): abrupt transitions
gradual transitions: dissolves, wipes, fade-in/out
camera movements: panning, tilting, zoom

11. Cut: Frames Between Shots
Furht. et.al 96
shot2

12. Dissolve: Transition Frames
Furht. et.al 96

13. 샷 검출 – 인접 프레임 비교 camera breaks 경우:
- compare color histograms of adjacent frames
gradual transitions 및 camera motion 경우:
- histograms are less successful

14. Grey level histograms for 3 successive frames
Frames 1 and 2 almost identical
Camera break between 2 and 3
Compute histogram differences

15. 칼라 영상 처리 Convert color to YUV color space and process intensity only:
I = 0.299R G B

16. Camera Breaks 검출 Pair-wise pixel comparison (intensities)
Histogram comparison for camera breaks
threshold selection

17. Camera Motion 검출
Motion vector analysis for camera motion and gradual transitions
E.G.M. Petrakis
Video Processing

18. Pair-Wise Pixel Comparison
Count pixels changed from a frame to the next
A shot boundary is found if more than Tb pixels changed
Problem: sensitivity to camera/object motion and noise
many pixels change

19. Pair-Wise Block Comparison
Compare blocks instead of pixels
μi ,μi+1: mean intensity values in frames
si,si+1: variances
Less sensitive to motion and noise

20. Pair-Wise Histogram Comparison
Furht. et.al 96
Even less sensitive to motion
i: frame count
j: intensity count in H
G=MxN intensities

21. Histogram Comparison
gradual
transitions
camera
breaks
Furht et.al. 96

22. Thresholds Tolerate variations while ensuring good performance
low thresholds accept many false positives
high thresholds reject true transitions
Threshold: varies from one video source to another
e.g., cartoons exhibit larger frame differences than films