Rethinking errors in human pose estimation heatmap

doi:10.11772/j.issn.1001-9081.2021050805

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (8): 2548-2555.DOI: 10.11772/j.issn.1001-9081.2021050805

• Multimedia computing and computer simulation • Previous Articles Next Articles

Rethinking errors in human pose estimation heatmap

Feiyu YANG¹^,²(), Zhan SONG¹, Zhenzhong XIAO², Yaoyang MO², Yu CHEN², Zhe PAN², Min ZHANG², Yao ZHANG², Beibei QIAN², Chaowei TANG³, Wu JIN³

^1.Shenzhen Institutes of Advanced Technology，Chinese Academy of Sciences，Shenzhen Guangdong 518055，China
^2.Orbbec Technology Incorporation Company Limited，Shenzhen Guangdong 518063，China
^3.College of Energy and Power Engineering，Nanjing University of Aeronautics and Astronautics，Nanjing Jiangsu 210016，China

Received:2021-05-17 Revised:2021-11-04 Accepted:2021-11-04 Online:2022-08-09 Published:2022-08-10
Contact: Feiyu YANG
About author:YANG Feiyu， born in 1990， Ph. D. His research interests include human pose estimation， image segmentation.
SONG Zhan， born in 1980， Ph. D.， professor. His research interests include computer vision.
XIAO Zhenzhong， born in 1980， Ph. D. His research interests include 3D perception and application.
MO Yaoyang， born in 1995， M. S. His research interests include human pose estimation， image segmentation.
CHEN Yu， born in 1988， M. S. His research interests include human pose estimation.
PAN Zhe， born in 1993， M. S. His research interests include human pose estimation.
ZHANG Min， born in 1994， M. S. Her research interests include human pose estimation.
ZHANG Yao， born in 1980， M. S. His research interests include software design.
QIAN Beibei， born in 1989， Ph. D. His research interests include computer vision.
TANG Chaowei， born in 1998， M. S. candidate. His research interests include numerical calculation.
JIN Wu， born in 1992， Ph. D.， associate professor. His research interests include numerical calculation.

对人体姿态估计热图误差的再思考

杨飞宇¹^,²(), 宋展¹, 肖振中², 莫曜阳², 陈宇², 潘哲², 张敏², 张遥², 钱贝贝², 汤朝伟³, 金武³

^1.中国科学院深圳先进技术研究院, 广东深圳 518055
^2.奥比中光科技集团股份有限公司, 广东深圳 518063
^3.南京航空航天大学能源与动力学院, 南京 210016

通讯作者: 杨飞宇
作者简介:杨飞宇（1990—），男，广东深圳人，博士，主要研究方向：人体姿态估计、图像分割；
宋展（1980—），男，广东深圳人，教授，博士，主要研究方向：计算机视觉；
肖振中（1980—），男，广东深圳人，博士，主要研究方向：3D感知与应用；
莫曜阳（1995—），男，广东东莞人，硕士，主要研究方向：人体姿态估计、图像分割；
陈宇（1988—），男，湖南衡阳人，硕士，主要研究方向：人体姿态估计；
潘哲（1993—），男，广东深圳人，硕士，主要研究方向：人体姿态估计；
张敏（1994—），女，陕西铜川人，硕士，主要研究方向：人体姿态估计；
张遥（1980—），男，广东深圳人，硕士，主要研究方向：软件设计；
钱贝贝（1989—），男，广东深圳人，博士，主要研究方向：计算机视觉；
汤朝伟（1998—），男，江苏南京人，硕士研究生，主要研究方向：数值计算；
金武（1992—），男，江苏南京人，副教授，博士，主要研究方向：数值计算。

Abstract

Abstract:

Recently， the leading human pose estimation algorithms are heatmap-based algorithms. Heatmap decoding （i.e. transforming heatmaps to coordinates of human joint points） is a basic step of these algorithms. The existing heatmap decoding algorithms neglect the effect of systematic errors. Therefore， an error compensation based heatmap decoding algorithm was proposed. Firstly， an error compensation factor of the system was estimated during training. Then， the error compensation factor was used to compensate the prediction errors including both systematic error and random error of human joint points in the inference stage. Extensive experiments were carried out on different network architectures， input resolutions， evaluation metrics and datasets. The results show that compared with the existing optimal algorithm， the proposed algorithm achieves significant accuracy gain. Specifically， by using the proposed algorithm， the Average Precision （AP） of the HRNet-W48-256×192 model is improved by 2.86 percentage points on Common Objects in COntext （COCO）dataset， and the Percentage of Correct Keypoints with respect to head （PCKh） of the ResNet-152-256×256 model is improved by 7.8 percentage points on Max Planck Institute for Informatics （MPII）dataset. Besides， unlike the existing algorithms， the proposed algorithm did not need Gaussian smoothing preprocessing and derivation operation， so that it is 2 times faster than the existing optimal algorithm. It can be seen that the proposed algorithm has applicable values to performing fast and accurate human pose estimation.

Key words: human pose estimation, heatmap, decoding, error compensation, neural network

摘要：

近年来，基于热图的算法一直占据人体姿态估计算法的主导地位。热图解码（即将热图转换为人体关节点坐标）算法是这类算法的基本环节。而当前的热图解码算法并没有考虑系统误差的影响，因此，提出一种基于误差补偿的人体姿态估计热图解码算法。首先在训练过程中评估模型的误差补偿因子，然后在推理阶段用误差补偿因子补偿人体关节点的预测误差，这些误差同时包括系统误差和随机误差。在不同的网络架构、输入分辨率、评估指标和数据集上进行的大量实验的结果表明与目前最佳的热图解码算法相比，所提算法获得了显著的精度增益。具体来说，所提算法使HRNet-W48-256×192模型在COCO（Common Objects in Context）数据集上的平均精度（AP）提升了2.86个百分点，使ResNet-152-256×256模型的相对于头部的正确点百分比指标在MPII（Max Planck Institute for Informatics）数据集上提升了7.8个百分点。此外，由于所提算法不像现存算法需要采用高斯平滑预处理和求导操作，因此速度约为当前最佳算法的2倍。可见，所提算法对于开展高精度、高速度的人体姿态估计具有实际的应用价值。

关键词: 人体姿态估计, 热图, 解码, 误差补偿, 神经网络

CLC Number:

TP391.4

Feiyu YANG, Zhan SONG, Zhenzhong XIAO, Yaoyang MO, Yu CHEN, Zhe PAN, Min ZHANG, Yao ZHANG, Beibei QIAN, Chaowei TANG, Wu JIN. Rethinking errors in human pose estimation heatmap[J]. Journal of Computer Applications, 2022, 42(8): 2548-2555.

杨飞宇, 宋展, 肖振中, 莫曜阳, 陈宇, 潘哲, 张敏, 张遥, 钱贝贝, 汤朝伟, 金武. 对人体姿态估计热图误差的再思考[J]. 《计算机应用》唯一官方网站, 2022, 42(8): 2548-2555.

Figures/Tables 10

Fig. 1 Signal and error functions of heatmap

Fig. 2 Calculation method of optimal compensation factor Δopt

Tab. 1 Optimal error compensation factor Δopt value of each model

模型	输入分辨率	$Δ o p t$
ResNet-50	256×192	4
	256×256	4
	384×288	5
ResNet-101	256×192	4
	256×256	4
	384×288	5
ResNet-152	256×192	4
	256×256	4
	384×288	5
HR-W32	256×192	4
HR-W32	384×288	5
HR-W48	256×192	4
HR-W48	384×288	5

Tab. 1 Optimal error compensation factor Δopt value of each model

模型	输入分辨率	$Δ o p t$
ResNet-50	256×192	4
	256×256	4
	384×288	5
ResNet-101	256×192	4
	256×256	4
	384×288	5
ResNet-152	256×192	4
	256×256	4
	384×288	5
HR-W32	256×192	4
HR-W32	384×288	5
HR-W48	256×192	4
HR-W48	384×288	5

Tab. 2 Accuracy on COCO validation dataset （validation process without flip strategy）

模型	输入分辨率	算法	精度/%
模型	输入分辨率	算法	AP	AP⁵⁰	AP⁷⁵	AP^M	AP^L	AR	AR⁵⁰	AR⁷⁵	AR^M	AR^L
ResNet-50	256×192	标准算法	65.34	90.37	74.48	63.25	68.59	69.32	91.85	77.96	66.57	73.48
		Shifting算法	66.80	90.43	75.74	65.15	70.28	70.84	91.99	78.90	68.09	75.00
		DARK算法	68.40	91.38	76.89	66.60	71.59	72.01	92.07	79.72	69.30	76.14
		本文算法	70.63	91.40	78.17	68.27	74.66	74.11	92.24	80.81	70.98	78.85
	384×288	标准算法	69.85	91.46	77.07	66.86	74.66	73.28	92.48	79.83	69.55	78.80
		Shifting算法	70.71	91.47	78.01	67.45	75.55	73.96	92.51	80.26	70.18	79.56
		DARK算法	71.49	91.47	78.20	68.43	76.50	74.71	92.66	80.79	70.93	80.35
		本文算法	72.92	91.52	79.41	69.20	78.45	75.80	92.87	81.72	71.72	81.86
ResNet-101	256×192	标准算法	66.60	91.45	75.77	65.21	69.60	70.54	92.46	78.84	68.04	74.35
		Shifting算法	68.43	91.44	77.89	66.77	71.40	72.06	92.44	80.05	69.60	75.86
		DARK算法	69.30	91.48	78.08	67.85	72.60	73.13	92.66	80.72	70.66	76.99
		本文算法	71.98	92.48	79.32	69.60	75.73	75.31	93.15	81.85	72.44	79.73
	384×288	标准算法	71.63	92.44	80.19	69.04	76.02	75.07	93.25	82.24	71.75	80.12
		Shifting算法	72.42	92.45	80.25	69.78	76.66	75.76	93.26	82.51	72.49	80.75
		DARK算法	73.22	92.47	80.35	70.70	77.68	76.51	93.31	82.97	73.20	81.56
		本文算法	74.52	92.47	81.40	71.44	79.40	77.55	93.42	83.61	73.97	82.99
ResNet-152	256×192	标准算法	67.42	91.48	76.75	65.51	70.85	71.26	92.66	79.83	68.63	75.28
		Shifting算法	68.86	91.52	77.86	67.10	72.23	72.60	92.85	80.68	70.02	76.55
		DARK算法	70.17	92.47	78.93	68.17	73.59	73.74	93.03	81.27	71.13	77.77
		本文算法	72.75	92.51	80.34	70.00	76.84	75.95	93.14	82.68	72.84	80.68
	384×288	标准算法	72.83	92.50	81.38	70.24	76.99	76.15	93.64	83.50	72.95	81.00
		Shifting算法	73.51	92.52	81.47	70.96	77.74	76.80	93.73	83.80	73.60	81.67
		DARK算法	74.26	92.54	82.44	71.88	78.63	77.50	93.77	84.32	74.34	82.31
		本文算法	75.48	92.54	82.59	72.57	80.33	78.50	93.84	84.70	75.05	83.75
HR-W32	256×192	标准算法	69.66	92.49	79.02	67.87	73.16	73.42	93.77	81.99	70.79	77.48
		Shifting算法	71.33	92.49	81.11	69.63	74.68	74.85	93.78	83.01	72.21	78.95
		DARK算法	72.74	92.51	81.41	70.85	76.57	76.24	93.83	83.82	73.46	80.53
		本文算法	75.47	93.49	83.50	72.86	79.52	78.35	94.05	85.11	75.26	83.13
	384×288	标准算法	73.53	92.54	82.21	71.24	77.74	76.94	93.88	84.15	73.69	81.92
		Shifting算法	74.45	92.54	82.33	71.84	78.62	77.69	93.92	84.49	74.45	82.66
		DARK算法	75.75	93.55	83.33	73.05	79.92	78.71	94.16	85.06	75.45	83.72
		本文算法	77.00	93.54	83.67	73.86	81.86	79.71	94.14	85.64	76.17	85.13
HR-W48	256×192	标准算法	69.86	92.48	79.79	68.12	73.31	73.70	93.73	82.31	70.90	77.92
		Shifting算法	71.53	92.50	81.03	69.56	75.05	75.23	93.78	83.28	72.38	79.55
		DARK算法	72.84	92.52	82.11	71.18	76.36	76.51	93.86	84.18	73.70	80.81
		本文算法	75.70	93.50	83.56	73.05	79.92	78.71	94.07	85.53	75.44	83.68
	384×288	标准算法	74.42	93.48	82.41	71.72	78.60	77.60	94.05	84.65	74.41	82.49
		Shifting算法	75.18	93.48	82.53	72.54	79.39	78.28	94.11	84.93	75.11	83.16
		DARK算法	76.15	93.50	83.69	73.59	80.46	79.15	94.11	85.67	75.99	84.02
		本文算法	77.23	93.52	83.74	74.15	82.25	80.07	94.24	85.97	76.61	85.41

Tab. 2 Accuracy on COCO validation dataset （validation process without flip strategy）

模型	输入分辨率	算法	精度/%
模型	输入分辨率	算法	AP	AP⁵⁰	AP⁷⁵	AP^M	AP^L	AR	AR⁵⁰	AR⁷⁵	AR^M	AR^L
ResNet-50	256×192	标准算法	65.34	90.37	74.48	63.25	68.59	69.32	91.85	77.96	66.57	73.48
		Shifting算法	66.80	90.43	75.74	65.15	70.28	70.84	91.99	78.90	68.09	75.00
		DARK算法	68.40	91.38	76.89	66.60	71.59	72.01	92.07	79.72	69.30	76.14
		本文算法	70.63	91.40	78.17	68.27	74.66	74.11	92.24	80.81	70.98	78.85
	384×288	标准算法	69.85	91.46	77.07	66.86	74.66	73.28	92.48	79.83	69.55	78.80
		Shifting算法	70.71	91.47	78.01	67.45	75.55	73.96	92.51	80.26	70.18	79.56
		DARK算法	71.49	91.47	78.20	68.43	76.50	74.71	92.66	80.79	70.93	80.35
		本文算法	72.92	91.52	79.41	69.20	78.45	75.80	92.87	81.72	71.72	81.86
ResNet-101	256×192	标准算法	66.60	91.45	75.77	65.21	69.60	70.54	92.46	78.84	68.04	74.35
		Shifting算法	68.43	91.44	77.89	66.77	71.40	72.06	92.44	80.05	69.60	75.86
		DARK算法	69.30	91.48	78.08	67.85	72.60	73.13	92.66	80.72	70.66	76.99
		本文算法	71.98	92.48	79.32	69.60	75.73	75.31	93.15	81.85	72.44	79.73
	384×288	标准算法	71.63	92.44	80.19	69.04	76.02	75.07	93.25	82.24	71.75	80.12
		Shifting算法	72.42	92.45	80.25	69.78	76.66	75.76	93.26	82.51	72.49	80.75
		DARK算法	73.22	92.47	80.35	70.70	77.68	76.51	93.31	82.97	73.20	81.56
		本文算法	74.52	92.47	81.40	71.44	79.40	77.55	93.42	83.61	73.97	82.99
ResNet-152	256×192	标准算法	67.42	91.48	76.75	65.51	70.85	71.26	92.66	79.83	68.63	75.28
		Shifting算法	68.86	91.52	77.86	67.10	72.23	72.60	92.85	80.68	70.02	76.55
		DARK算法	70.17	92.47	78.93	68.17	73.59	73.74	93.03	81.27	71.13	77.77
		本文算法	72.75	92.51	80.34	70.00	76.84	75.95	93.14	82.68	72.84	80.68
	384×288	标准算法	72.83	92.50	81.38	70.24	76.99	76.15	93.64	83.50	72.95	81.00
		Shifting算法	73.51	92.52	81.47	70.96	77.74	76.80	93.73	83.80	73.60	81.67
		DARK算法	74.26	92.54	82.44	71.88	78.63	77.50	93.77	84.32	74.34	82.31
		本文算法	75.48	92.54	82.59	72.57	80.33	78.50	93.84	84.70	75.05	83.75
HR-W32	256×192	标准算法	69.66	92.49	79.02	67.87	73.16	73.42	93.77	81.99	70.79	77.48
		Shifting算法	71.33	92.49	81.11	69.63	74.68	74.85	93.78	83.01	72.21	78.95
		DARK算法	72.74	92.51	81.41	70.85	76.57	76.24	93.83	83.82	73.46	80.53
		本文算法	75.47	93.49	83.50	72.86	79.52	78.35	94.05	85.11	75.26	83.13
	384×288	标准算法	73.53	92.54	82.21	71.24	77.74	76.94	93.88	84.15	73.69	81.92
		Shifting算法	74.45	92.54	82.33	71.84	78.62	77.69	93.92	84.49	74.45	82.66
		DARK算法	75.75	93.55	83.33	73.05	79.92	78.71	94.16	85.06	75.45	83.72
		本文算法	77.00	93.54	83.67	73.86	81.86	79.71	94.14	85.64	76.17	85.13
HR-W48	256×192	标准算法	69.86	92.48	79.79	68.12	73.31	73.70	93.73	82.31	70.90	77.92
		Shifting算法	71.53	92.50	81.03	69.56	75.05	75.23	93.78	83.28	72.38	79.55
		DARK算法	72.84	92.52	82.11	71.18	76.36	76.51	93.86	84.18	73.70	80.81
		本文算法	75.70	93.50	83.56	73.05	79.92	78.71	94.07	85.53	75.44	83.68
	384×288	标准算法	74.42	93.48	82.41	71.72	78.60	77.60	94.05	84.65	74.41	82.49
		Shifting算法	75.18	93.48	82.53	72.54	79.39	78.28	94.11	84.93	75.11	83.16
		DARK算法	76.15	93.50	83.69	73.59	80.46	79.15	94.11	85.67	75.99	84.02
		本文算法	77.23	93.52	83.74	74.15	82.25	80.07	94.24	85.97	76.61	85.41

Fig. 3 AP comparison of different methods on COCO validation dataset

Fig. 4 Influence of error compensation on prediction results

Tab. 3 PCKh accuracy on MPII dataset （validation process without flip strategy）

模型	算法	PCKh
模型	算法	Head	Shoul.	Elbow	Wrist	Hip	Knee	Ankle	PCKh^0.1	PCKh^0.5
ResNet-50	标准算法	96.04	94.19	87.25	81.34	86.15	81.60	78.32	21.55	86.99
	Shifting算法	96.04	94.34	87.35	81.53	86.41	81.85	78.48	23.40	87.15
	DARK算法	96.15	94.53	87.76	81.87	86.76	82.49	78.81	24.48	87.48
	本文算法	95.87	94.87	88.44	82.05	87.62	83.22	79.48	31.69	87.95
ResNet-101	标准算法	96.35	94.62	87.40	82.41	85.72	82.35	78.77	22.07	87.36
	Shifting算法	96.59	94.58	87.69	82.39	86.22	82.71	78.98	23.66	87.56
	DARK算法	96.32	94.72	88.07	82.85	86.71	83.16	79.24	24.82	87.85
	本文算法	96.28	94.80	88.55	83.42	87.54	83.42	79.74	32.09	88.25
ResNet-152	标准算法	96.62	95.02	88.27	82.70	86.38	83.30	79.85	22.55	87.98
	Shifting算法	96.62	95.31	88.56	82.99	86.91	83.58	79.83	24.31	88.23
	DARK算法	96.73	95.33	88.80	83.66	87.02	83.78	80.63	25.28	88.50
	本文算法	96.56	95.67	88.97	83.85	87.99	84.14	80.52	33.07	88.78
HR-W32	标准算法	96.79	95.06	89.08	84.29	86.01	84.40	81.39	23.49	88.61
	Shifting算法	96.93	95.25	89.06	84.39	86.43	84.89	81.58	25.36	88.81
	DARK算法	96.97	95.40	89.57	85.03	87.04	85.67	82.03	27.38	89.25
	本文算法	96.86	95.58	89.98	85.49	87.83	86.18	82.59	35.80	89.67

Fig. 5 PCKh comparison of different methods on MPII dataset

Tab. 4 Influence of resolution， Gaussian smoothing and backbone network on algorithm accuracy

算法	分辨率	高斯平滑	ResNet-50		ResNet-101		ResNet-152		HR-W32
算法	分辨率	高斯平滑	精度/%	相差百分点	精度/%	相差百分点	精度/%	相差百分点	精度/%	相差百分点
DARK算法	256×192	$√$	68.40	0.52↓	69.30	0.09↓	70.17	0.26↓	72.74	0.50↓
	256×192	$×$	67.88	0.52↓	69.21	0.09↓	69.91	0.26↓	72.24	0.50↓
	384×288	$√$	71.49	0.41↓	73.22	0.24↓	74.26	0.13↓	75.75	0.75↓
	384×288	$×$	71.08	0.41↓	72.98	0.24↓	74.13	0.13↓	75.00	0.75↓
本文算法	256×192	$√$	70.69	0.06↓	71.96	0.02↑	72.76	0.01↓	75.43	0.04↑
	256×192	$×$	70.63	0.06↓	71.98	0.02↑	72.75	0.01↓	75.47	0.04↑
	384×288	$√$	72.84	0.08↑	74.53	0.01↓	75.31	0.17↑	77.01	0.01↓
	384×288	$×$	72.92	0.08↑	74.52	0.01↓	75.48	0.17↑	77.00	0.01↓

Tab. 4 Influence of resolution， Gaussian smoothing and backbone network on algorithm accuracy

算法	分辨率	高斯平滑	ResNet-50		ResNet-101		ResNet-152		HR-W32
算法	分辨率	高斯平滑	精度/%	相差百分点	精度/%	相差百分点	精度/%	相差百分点	精度/%	相差百分点
DARK算法	256×192	$√$	68.40	0.52↓	69.30	0.09↓	70.17	0.26↓	72.74	0.50↓
	256×192	$×$	67.88	0.52↓	69.21	0.09↓	69.91	0.26↓	72.24	0.50↓
	384×288	$√$	71.49	0.41↓	73.22	0.24↓	74.26	0.13↓	75.75	0.75↓
	384×288	$×$	71.08	0.41↓	72.98	0.24↓	74.13	0.13↓	75.00	0.75↓
本文算法	256×192	$√$	70.69	0.06↓	71.96	0.02↑	72.76	0.01↓	75.43	0.04↑
	256×192	$×$	70.63	0.06↓	71.98	0.02↑	72.75	0.01↓	75.47	0.04↑
	384×288	$√$	72.84	0.08↑	74.53	0.01↓	75.31	0.17↑	77.01	0.01↓
	384×288	$×$	72.92	0.08↑	74.52	0.01↓	75.48	0.17↑	77.00	0.01↓

Tab. 5 Extra running time of different methods beyond standard method

算法	每帧额外耗时
Shifting算法	0.3
DARK算法	3.0
本文算法	1.4

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Rethinking errors in human pose estimation heatmap

对人体姿态估计热图误差的再思考

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