Shot boundary detection in the framework of rough indexing paradigm
Résumé
This paper presents the Shot Boundary Detection system developed by LaBRI in the context of “Rough Indexing” paradigm. We work on compressed streams and we use only I and P frames information, (DC coefficients of I-Frames, motion vectors of PFrames and DC coefficients of prediction error) which allow us to be faster than many equivalent systems (10 times faster than real-time on TRECVID2003 test set, and 3 times faster on 2004, because MPEG files structure is composed of only I and P
frames). In this context the application was not developed to classify shot change transition effects, the initial goal was to allow a real-time and intelligent browsing in video content for common users.
The detection is performed in two stages:
- Robust Global Camera Motion Estimation
- Detection of P-Frame peaks (computation of motion and frame statistics), and of I-Frames (measuring similarity on successive compensated I frames).
As we work with two types of frames (I and P), we associate two statistical models which give us two sets of ratio and threshold to calibrate the detector.
The first TRECVID participation of LaBRI implies an evolution of the application for transitions effects distinction, which induces two new thresholds to calibrate. We generally obtain equivalent values of Recall and Precision (0.72 on TRECVID 2003 test set). On TRECVID 2004 test set we obtain as best runs ri-3: 0.723(Recall) and 0.606(Precision); and ri-4: 0.703(Recall) and 0.635(Precision).