基于混合遗传算法的人体运动捕获数据关键帧提取

Keyframe Extraction from Human Motion Capture Data by Simplex Hybrid Genetic Algorithm

为实现基于最佳关键帧集合的人体运动的紧致表示,提出一种遗传算法与单纯形法结合的人体运动捕获数据关键帧提取方法.以重构误差最小化和压缩率最优为目标,定义适应度函数,度量重构运动与原始运动之间的重构误差,通过关节位置和关节速率加权计算,并考虑数据的运动特性.利用背景知识对初始种群的个体进行优化,保证进化的良好基础和种群的多样性.将遗传算法和局部搜索技术结合,提高算法运行效率和求解质量.实验结果表明,该方法能够高效地从运动捕获数据中提取出最优的关键帧集合,较好地满足运动数据的紧致表示,且能高质量重构其它帧.

To obtain a compact representation of human motion based on keyframes, a method for keyframes extracting of the captured human motion data by simplex hybrid genetic algorithm is presented, which combines genetic algorithm with a local search technique to converge faster and produce the optimal solution. Firstly, the fitness function is defined to evaluate the availability of keyframe with the goals of minimal reconstruction error and optimal compression rate. Then, the reconstruction error is computed between the original motion and the reconstruction one by the weighted differences of joint positions and velocities. The velocity term helps to preserve the dynamics of motion. Finally, the individuals of initial population are optimized by the knowledge to assure the evolutionary efficiency and the population diversity. Experimental results show that the proposed method remarkable results in terms of quality and compression ratio, can effectively extract keyframes, produce and reconstruct all other non-keyframes of an animation with these keyframes.