<title>Improvement of shot detection methods based on dynamic threshold selection</title>

Abstract

Currently, most shot detection methods proposed in the literature are based on well-chosen static thresholds on which the quality of result largely depends. In this paper, we present a method for dynamic threshold selection based on clustering a set of N points on a comparison curve, which we sue for characteristic feature comparison through images in a video sequence to detect shots In this method we recursively chose N successive values from the curve. Then by using the clustering method on them, we partition this set into two parts, larger values in E1, and smaller values in E2. We try to model the form of the curve as a bimodal one, and try to find a threshold around a valley area. Using above clustering analysis, we first apply color histogram (CH) and double Hough transformation (DHT) that we reported in our previous work on 90 minutes of video sequence. The experimental results show that dynamic threshold based methods improve the static threshold based ones, reducing false and missed detection, and that dynamic threshold based DHT is more robust than dynamic threshold based CH.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.