1. 3/6/2015 PortoICIAR’20041 Adaptive Methods for Motion Characterization and Segmentation of MPEG Compressed Frame Sequences C. Doulaverakis, S. Vagionitis, M. Zervakis, E. Petrakis Technical University of Crete (TUC) Chania Crete Greece

2. 3/6/2015 PortoICIAR’20042 Problem Definition  Video segmentation  Abrupt (cuts) & Gradual transitions  Zoom, Pan/Tilt  Contribution: segmentation and identification of camera effects  Processing on MPEG video  Partially decompressed block data  DC intensity approximation for blocks  Coherent motion vectors for I, P, B frames

3. 3/6/2015 PortoICIAR’20043 Twin Comparison (TC)  Shot boundaries peaks on histogram differences  Thresholds: Ta, Tb  Ta = μ + α σ  Τb = bμ  Requires pre- processing,does not adapt to signal Τα Tb

4. 3/6/2015 PortoICIAR’20044 Sliding Window (SW)  Processing over W frames  One Threshold: Ta(i) = μ(i) + α σ(i)  Cut: < 5 frames  Gradual: > 5 frames  No preprocessing, adapts to signal Ta

5. 3/6/2015 PortoICIAR’20045 Adaptive Method (AM)  Ta(i) = μ(i) + α σ(i) μ(i) =μ(i-1)-c(μ(i-1)–D(i)) σ(i) = |μ(i) 2 – λ(i)| 1/2 λ(i)=λ(i-1)–c (λ(i-1)–D(i)) 2 c=0.05  Ta is computed at each i and depends on previous values  No preprocessing, adapts to signal Ta

6. 3/6/2015 PortoICIAR’20046 Direction Histogram  Histogram of angles of motion vectors  8 angles multiples of π/4 for moving vectors  Plus 0-th value for static vectors |v| < 1 static moving

7. 3/6/2015 PortoICIAR’20047 Motion Characterization  Analysis of variance σ motion histogram  Normalized by number of intracoded vectors  Zooming: the vectors are spread uniformely (max σ)  Panning-Tilting: the vectors are concentrated at a single bin (min σ)  Static camera: the vectors are concentrated at bin 0

8. 3/6/2015 PortoICIAR’20048 Example pan pan zoom static camera Ta

9. 3/6/2015 PortoICIAR’20049 Video Segmentation Method  Cuts: the number of intracoded vectors in frame exceeds threshold  Gradual transitions: combines motion and intensity information  Difference of intensity histogram exceeds threshold  Magnitude of motion vectors exceed threshold

10. 3/6/2015 PortoICIAR’200410 Experiments  Measurements over 17 videos  Competitive methods correspond to thresholding by TC, SW, AM  Each method is represented by its precision/recall curve as a function of the threshold parameter a

11. 3/6/2015 PortoICIAR’200411 Abrupt Transitions (cuts)

12. 3/6/2015 PortoICIAR’200412 Gradual Transitions

13. 3/6/2015 PortoICIAR’200413 Future Work  More accurate threshold estimation  SW or AM gets trapped in local minima  Detection of Cuts is fairly stable  More elaborate methods for detection of gradual transitions and for cleaning- up false positives due to camera effects

14. 3/6/2015 PortoICIAR’200414 Zoom Detection α=4.5 α=2

15. 3/6/2015 PortoICIAR’200415 Pan/Tilt Detection α=2 α=4.5