An approach for complex activity recognition by key frames
来源期刊：中南大学学报(英文版)2015年第9期
论文作者：XIA Li-min SHI Xiao-ting TU Hong-bin
文章页码：3450 - 3457
Key words：human activity recognition; complex activity segmentation; key frame extraction
Abstract: A new method for complex activity recognition in videos by key frames was presented. The progressive bisection strategy (PBS) was employed to divide the complex activity into a series of simple activities and the key frames representing the simple activities were extracted by the self-splitting competitive learning (SSCL) algorithm. A new similarity criterion of complex activities was defined. Besides the regular visual factor, the order factor and the interference factor measuring the timing matching relationship of the simple activities and the discontinuous matching relationship of the simple activities respectively were considered. On these bases, the complex human activity recognition could be achieved by calculating their similarities. The recognition error was reduced compared with other methods when ignoring the recognition of simple activities. The proposed method was tested and evaluated on the self-built broadcast gymnastic database and the dancing database. The experimental results prove the superior efficiency.
XIA Li-min(夏利民), SHI Xiao-ting(时晓亭), TU Hong-bin(涂宏斌)
(School of Information Science and Engineering, Central South University, Changsha 410075, China)
Abstract:A new method for complex activity recognition in videos by key frames was presented. The progressive bisection strategy (PBS) was employed to divide the complex activity into a series of simple activities and the key frames representing the simple activities were extracted by the self-splitting competitive learning (SSCL) algorithm. A new similarity criterion of complex activities was defined. Besides the regular visual factor, the order factor and the interference factor measuring the timing matching relationship of the simple activities and the discontinuous matching relationship of the simple activities respectively were considered. On these bases, the complex human activity recognition could be achieved by calculating their similarities. The recognition error was reduced compared with other methods when ignoring the recognition of simple activities. The proposed method was tested and evaluated on the self-built broadcast gymnastic database and the dancing database. The experimental results prove the superior efficiency.
Key words:human activity recognition; complex activity segmentation; key frame extraction
