For multi-person 2D pose estimation,current deep learning baised methods have exhibited impressive performance,but the trade-offs among efficiency,robustness,and accuracy in the existing approaches remain unavoidable....For multi-person 2D pose estimation,current deep learning baised methods have exhibited impressive performance,but the trade-offs among efficiency,robustness,and accuracy in the existing approaches remain unavoidable.In principle,bottom-up methods are superior to top-down methods in efficiency,but they perform worse in accuracy.To make full use of their respective advantages,in this paper we design a novel bidirectional optimization coupled lightweight network(BOCLN)architecture for efficient,robust,and general-purpose multi-person 2D(2-dimensional)pose estimation from natural images.With the BOCLN framework,the bottom-up network focuses oil global features,while the top-down net work places emphasis on det ailed features.The entire framework shares global features along the bottom-up data stream,while the top-down data stream aims to accelerate the accurate pose estimation.In particular,to exploit the priors of human joints'relationship,we propose a probability limb heat map to represent the spatial context of the joints and guide the overall pose skeleton prediction,so that each person's pose estimation in cluttered scenes(involving crowd)could be as accurate and robust as possible.Therefore,benefiting from the novel BOCLN architecture,the tinie-consuming refinement procedure could be much simplified to an efficient lightweight network.Extensive experiments and evaluations on public benchmarks have confirmed that our new method is more efficient and robust,yet still attain competitive accuracy performance compared with the state-of-the-art methods.Our BOCLN shows even greater promise in online applications.展开更多
基金the National Natural Science Foundation of China under Grant Nos.61672077 and 61532002the Applied Basic Research Program of Qingdao under Grant No.161013xxthe National Science Foundation of USA under Grant Nos.US-0949467.IIS-1047715,IIS-1715985,IIS61672149,and IIS-1049448.
文摘For multi-person 2D pose estimation,current deep learning baised methods have exhibited impressive performance,but the trade-offs among efficiency,robustness,and accuracy in the existing approaches remain unavoidable.In principle,bottom-up methods are superior to top-down methods in efficiency,but they perform worse in accuracy.To make full use of their respective advantages,in this paper we design a novel bidirectional optimization coupled lightweight network(BOCLN)architecture for efficient,robust,and general-purpose multi-person 2D(2-dimensional)pose estimation from natural images.With the BOCLN framework,the bottom-up network focuses oil global features,while the top-down net work places emphasis on det ailed features.The entire framework shares global features along the bottom-up data stream,while the top-down data stream aims to accelerate the accurate pose estimation.In particular,to exploit the priors of human joints'relationship,we propose a probability limb heat map to represent the spatial context of the joints and guide the overall pose skeleton prediction,so that each person's pose estimation in cluttered scenes(involving crowd)could be as accurate and robust as possible.Therefore,benefiting from the novel BOCLN architecture,the tinie-consuming refinement procedure could be much simplified to an efficient lightweight network.Extensive experiments and evaluations on public benchmarks have confirmed that our new method is more efficient and robust,yet still attain competitive accuracy performance compared with the state-of-the-art methods.Our BOCLN shows even greater promise in online applications.
