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摘要: 人体姿态估计是计算机视觉领域的一个研究热点，在行为识别、人机交互等领域均有广泛的应用.本文综合粗、细粒度模型的优点，以人体部件轨迹片段为实体构建中粒度时空模型，通过迭代的时域和空域交替解析，完成模型的近似推理，为每一人体部件选择最优的轨迹片段，拼接融合形成最终的人体姿态序列估计.为准备高质量的轨迹片段候选，本文引入全局运动信息将单帧图像中的最优姿态检测结果传播到整个视频形成轨迹，然后将轨迹切割成互相交叠的固定长度的轨迹片段.为解决对称部件易混淆的问题，从概念上将模型中的对称部件合并，在保留对称部件间约束的前提下，消除空域模型中的环路.在三个数据集上的对比实验表明本文方法较其他视频人体姿态估计方法达到了更高的估计精度.Abstract: Human pose estimation has attracted much attention in the computer vision community due to its potential applications in action recognition, human-computer interaction, etc. To focus on pose estimation in videos, a medium granularity spatio-temporal probabilistic graphical model using body part tracklets as entities is presented in this paper. The optimal tracklet for each body part is acquired by spatiotemporal approximate reasoning through iterative spatial and temporal parsing, and the final human pose estimation is achieved by merging these optimal tracklets. To generate reliable tracklet proposals, global motion cue is adopted to propagate pose detections from individual frames to the whole video, and the trajectories from this propagation are segmented into fixed-length overlapping tracklets. To deal with the double counting problem, symmetric parts are coupled to one virtual node, so that the loops in spatial model are removed and the constaints between symmetric parts are maintained. The experiment on three datasets shows the proposed method achieves a higher accuracy than other pose estimation methods.1) 本文责任编委 王亮
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图 4 不同方法的长时运动估计对比
Fig. 4 Long-term performances of different motion estimation approaches
图 3 不同方法的短时运动估计对比
Fig. 3 Short-term performances of different motion estimation approaches
图 5 基于中粒度模型的视频人体姿态估计方法示意图
Fig. 5 Overview of the video pose estimation method based on medium granularity model
表 1 UnusualPose视频集上的PCK评分对比
Table 1 PCK on UnusualPose dataset
Method Head Shld. Elbow Wrist Hip Knee Ankle Avg Nbest 99.8 99.4 76.2 65.0 87.8 70.8 71.5 81.5 UVA 99.4 93.8 72.7 56.2 89.3 66.3 62.4 77.2 PE_GM 98.7 98.3 89.9 73.8 91.0 76.4 88.9 88.1 Ours 98.7 98.1 90.1 75.1 95.9 88.4 89.5 90.8 
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表 2 FYDP视频集上的PCK评分对比
Table 2 PCK on FYDP dataset
Method Head Shld. Elbow Wrist Hip Knee Ankle Avg Nbest 95.7 89.7 75.2 59.1 83.3 81.4 79.5 80.6 UVA 96.2 91.7 78.4 60.3 85.4 83.8 79.2 82.1 PE_GM 98.4 89.2 80.9 60.5 84.4 89.3 83.7 83.8 Ours 97.9 93.4 84 63.1 88.4 88.9 84.4 85.7
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表 3 Sub_Nbest视频集上的PCP评分对比
Table 3 PCP on Sub_Nbest dataset
Method Head Torso U.A. L.A. U.L. L.L. Nbest 100 61.0 66.0 41.0 86.0 84.0 SYM 100 69.0 85.0 42.0 91.0 89.0 PE_GM 100 97.9 97.9 67.0 94.7 86.2 HPEV 100 100 93.0 65.0 92.0 94.0 Ours 100 98.1 96.6 58.6 95.1 94.8
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