Human Motion Data Retrieval Based on Staged Dynamic Time Deformation Optimization Algorithm

Abstract

In recent years, with the rapid development of computer storage capabilities and network transmission capabilities, users can easily share their own video and image information on social networking sites, and the amount of multimedia data on the network is rapidly increasing. With the continuous increase of the amount of data in the network, the establishment of effective automated data management methods and search methods has become an increasingly urgent need. This paper proposes a retrieval method of human motion data based on motion capture in index space. By extracting key frames from the original motion to perform horizontal dimensionality reduction and defining features based on Laban motion analysis, the motion segment is subjected to vertical feature dimensionality reduction. After extracting features from the input motion segment, motion matching is performed on the index space. This paper designs the optimization method of the phased dynamic time deformation algorithm in time efficiency and analyzes the optimization method of the phased dynamic time deformation algorithm in time complexity. Considering the time efficiency redundancy, this paper optimizes the time complexity of the phased dynamic time deformation method. This improves the time efficiency of the staged dynamic time warping algorithm, making it suitable for larger-scale human motion data problems. Experiments show that the method in this paper has the advantage of speed, is more in line with the semantics of human motion, and can meet the retrieval requirements of human motion databases.

An Efficient Dynamic Regulated Fuzzy Neural Network for Human Motion Retrieval and Analysis

“…To describe local motion accurately, motion features based on frames are developed. Based on the extracted features on key frames, Bao et al [4] presented a retrieval method of human motion data. In order to better description of 3D human motion, Ramezani et al [5] retrieved human action using a low-complexity representation based on the extracted local features.…”