Automatic Identification of Sports Video Highlights using Viewer Interest Features

Classification of viewer interest using facial expression and heart rate facilitates automatic identification of interest evoking video segments. Sports video is suitable for testing the effectiveness of such a system as it has structured segments with distinguishable highlight events. Previous work has not investigated the differences in viewer interest characteristics from one sports type to another, which is crucial for appropriate classification methodology. Thus, it is still unclear whether a universal classification model can be used for analyzing viewer interest for all types of sports. This paper addresses this gap by demonstrating a significant difference (p < 0.05) in the distributions of viewer interest data in soccer compared to tennis. Based on this finding, this paper proposes an adoption of Gaussian mixture models (GMM) to integrate sports-specific and sports-independent approaches for identifying video segments, which would be of potential interest to individual viewers. The approaches achieve 52% to 64% accuracy, demonstrating that sports-specific approach gets better performance.