Performance Evaluation of Shot Boundary Detection Metrics in the Presence of Object and Camera Motion

Abstract

Partitioning a video into shots is an important step for video indexing. We have presented the performance of various traditional metrics that are generally used to detect shot boundaries. In this paper, we evaluated shot boundary detection metrics, such as likelihood ratio and color ratio histogram in Red Green Blue (RGB) and Hue Saturation, Value (HSV) color space for three different action and thriller movies. These movies consist of large number of frames with object and camera motion. The pixel difference and Chi-square shot boundary detection metrics in Luma and Chrominance Components (YUV) color space has been tested for Ave different movies. The results were evaluated in terms of Recall, Precision, and F1 measure for all these movies. It has been observed that these results are affected by the disturbance due to the motion in the consecutive frames. The false positives and miss detection of shot boundaries in all the tested metrics are due to fast camera and object motion. An algorithm has been proposed for shot boundary detection by using dual tree complex wavelet transform in the presence of motion. Performance comparison of the proposed algorithm with the traditional metrics validates its effectiveness in terms of improved Recall, Precision, and F1 score.