Start and End Point Detection of Weightlifting Motion using CHLAC and MRA

Abstract

Extracting human motion segments of interest in image sequences is essential for quantitative analysis and effective video browsing, although it requires laborious human efforts. In analysis of sport motion such as weightlifting, it is required to detect the start and end of each weightlifting motion in an automated manner and hopefully even for different camera angleviews. This paper describes a weightlifting motion detection method employing cubic higher-order local auto-correlation (CHLAC) and multiple regression analysis (MRA). This method extracts spatio-temporal motion features and leans the relationship between the features and specific motion, without prior knowledge about objects. To demonstrate the effectiveness of our method, the experiment was conducted on data captured from eight different viewpoints in practical situations. The detection rates for the start and end motions were more than 94% for 140 data in total even for different angle views, 100% for some angles.