Reducing False Positives in Video Shot Detection Using Learning Techniques

Video has become an interactive medium of daily use today. However, the sheer volume of the data makes it extremely difficult to browse and find required information. Organizing the video and locating required information effectively and efficiently presents a great challenge to the video retrieval community. This demands a tool which would break down the video into smaller and manageable units called shots.

Traditional shot detection methods use pixel difference, histograms, or temporal slice analysis to detect hard-cuts and gradual transitions. However, systems need to be robust to sequences that contain dramatic illumination changes, shaky camera effects, and special effects such as fire, explosion, and synthetic screen split manipulations. Traditional systems produce false positives for these cases; i.e., they claim a shot break when there is none.

We propose a shot detection system which reduces false positives even if all the above effects are

cumulatively

present in one sequence. Similarities between successive frames are computed by finding the correlation and is further analyzed using a wavelet transformation. A final filtering step is to use a trained Support Vector Machine (SVM). As a result, we achieve better accuracy (while retaining speed) in detecting shot-breaks when compared with other techniques.