短道速滑场景下的多目标跟踪与运动仿真

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针对短道速滑比赛场景难以获取各运动员运动数据的问题,构建一个多目标跟踪与运动仿真的算法流程。提出一种局部匹配度量方法,处理单视角视频内存在的局部遮挡问题,提升跟踪的稳定性与鲁棒性。基于单应映射和拟合曲线求导的运动仿真方法,估算速度与加速度等运动学参数,实现运动仿真。在短道数据集的仿真实验中,验证了所提方法的有效性与优越性。
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兴趣匹配在分布式仿真中扮演着重要的角色。然而,在大规模仿真场景中,仿真实体数量大且区域变化频繁,导致兴趣匹配的计算量很大,严重影响仿真性能。另一方面,多核处理器的普及也促使从并行视角来提升兴趣匹配算法的性能。针对上述问题,提出了一种并行层次兴趣匹配算法,将订阅区域映射到一棵满二叉树中,由更新区域并行地与二叉树进行匹配,并利用二叉树相邻节点的区域关联关系来剔除不必要的计算。实验结果表明:并行层次兴趣
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