Analysis-by-synthesis dissolve detection

This paper presents a novel, real-time, minimal-latency technique for dissolve detection which handles the widely varying camera techniques, expertise, and overall video quality seen in amateur, semi-professional, and professional video footage. We achieve 88 % recall and 93 % precision for dissolve detection. In contrast, on the same data set, at a similar recall rate (87%), DCD has more than 3 times the number of false positives, giving a precision of only 81 % for dissolve detection. 1. OVERVIEW This paper discusses an improved approach for dissolve detection. A dissolve gradually cross-fades from the old shot’s footage to the new shot’s footage. The dissolve is the most common transition used in post-production. It is also available as an “in-camera ” effect on many consumer-grade camcorders. We use the results from our dissolve detector (along with our cut and fade detectors) to support scene-based video browsing and editing [1]. By placing our detector at the heart of an inexpensive consumer product, we have been forced to make it both computationally efficient and robust to the widely varying camera techniques, expertise, and video quality seen in amateur and semi-professional footage. This paper does not describe our approach to cut or fade detection due to the extensive and successful prior art [2,3]. Instead, after a short introduction to dissolve detection (Section 2), we describe our new approach (Section 3). Section 4 presents our precision and recall for dissolve detection and compares these to the results we get using the best published approach. Section 5 concludes by summarizing our approach. 2. BACKGROUND Many approaches to dissolve detection have been published over the years. The published approaches to dissolve detection fall into 3 broad categories