Abstract
AbstractThe key frames extraction technique extracts key postures to describe the original motion sequence, which has been widely used in motion compression, motion retrieval, motion edition and so on. In this paper, we propose a method based on the amplitude of curve to find key frames in a motion captured sequence. First we select a group of joint distance features to represent the motion and adopt the Principal Component Analysis (PCA) method to obtain the one dimension principal component as a features curve which will be used. Then we gain the initial key-frames by extracting the local optimum points in the curve. At last, we get the final key frames by inserting frames based on the amplitude of the curve and merging key frames too close. A number of experimental examples demonstrate that our method is practicable and efficient not only in the visual performance but also in the aspect of the compression ratio and error rate.
Highlights
In the last few decades, the technique of human motion capture develops rapidly and the significance of motion capture for several applications is rising
This paper focuses on key-frames extraction method for human motion capture data
Avrithis et al.[21]extracted key frames from MPEG video databases based on a multiresolution implementation of the recursive shortest spanning tree (RSST) algorithm, and using a fuzzy multidimensional histogram
Summary
In the last few decades, the technique of human motion capture develops rapidly and the significance of motion capture for several applications is rising. The key-frames extraction technique plays an irreplaceable role in representation of the whole animation sequence and is very beneficial for storage, compression, retrieval, browse and reuse for human Mocap data. There have been a considerable number of approaches proposed for extraction of key frames They essentially differ from each other in the way that they treat motion sequences. We compare those solutions in the related work section. This paper focuses on key-frames extraction method for human motion capture data. Key-frames collection can be utilized for reconstructing the original motion sequence as precisely as possible. We choose a new distance characteristic curve to reflect motion essential features. We automatically extract a certain number of key frames
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