Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios.With the introduction of end-to-end direct regression methods,the fi...Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios.With the introduction of end-to-end direct regression methods,the field has entered a new stage of development.However,the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal method.In this paper,we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy,which is applied to themulti-viewmulti-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external factors.Specifically,it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy,which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding joints.We call this method as the Efficient Recalibration Network(ER-Net).Finally,experiments were conducted on two benchmark datasets for this task,Campus and Shelf,in which the PCP reached 97.3% and 98.3%,respectively.展开更多
In human-robot collaborative tasks,human trust in robots can reduce resistance to them,thereby increasing the success rate of task execution.However,most existing studies have focused on improving the success rate of ...In human-robot collaborative tasks,human trust in robots can reduce resistance to them,thereby increasing the success rate of task execution.However,most existing studies have focused on improving the success rate of humanrobot collaboration(HRC)rather than on enhancing collaboration efficiency.To improve the overall collaboration efficiency while maintaining a high success rate,this study proposes an active interaction strategy generation for HRC based on trust.First,a trust-based optimal robot strategy generation method was proposed to generate the robot’s optimal strategy in a HRC.This method employs a tree to model the HRC process under different robot strategies and calculates the optimal strategy based on the modeling results for the robot to execute.Second,the robot’s performance was evaluated to calculate human’s trust in a robot.A robot performance evaluation method based on a visual language model was also proposed.The evaluation results were input into the trust model to compute human’s current trust.Finally,each time an object operation was completed,the robot’s performance evaluation and optimal strategy generation methods worked together to automatically generate the optimal strategy of the robot for the next step until the entire collaborative task was completed.The experimental results demonstrates that this method significantly improve collaborative efficiency,achieving a high success rate in HRC.展开更多
基金supported in part by the Key Program of NSFC (Grant No.U1908214)Special Project of Central Government Guiding Local Science and Technology Development (Grant No.2021JH6/10500140)+3 种基金Program for the Liaoning Distinguished Professor,Program for Innovative Research Team in University of Liaoning Province (LT2020015)Dalian (2021RT06)and Dalian University (XLJ202010)the Science and Technology Innovation Fund of Dalian (Grant No.2020JJ25CY001)Dalian University Scientific Research Platform Project (No.202101YB03).
文摘Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios.With the introduction of end-to-end direct regression methods,the field has entered a new stage of development.However,the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal method.In this paper,we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy,which is applied to themulti-viewmulti-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external factors.Specifically,it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy,which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding joints.We call this method as the Efficient Recalibration Network(ER-Net).Finally,experiments were conducted on two benchmark datasets for this task,Campus and Shelf,in which the PCP reached 97.3% and 98.3%,respectively.
基金supported in part by the National Key Research and Development Program of China,No.2021ZD0112400Support Plan for Key Field Innovation Team of Dalian,China,No.2021RT06+3 种基金Support Plan for 111 Project,No.D23006Dalian Major Projects of Basic Research,No.2023 JJ11 CG002China’s National Foreign Experts Project,No.D20240244Liaoning Province Education Department Basic Scientific Research Projects,No.LJ232411258019.
文摘In human-robot collaborative tasks,human trust in robots can reduce resistance to them,thereby increasing the success rate of task execution.However,most existing studies have focused on improving the success rate of humanrobot collaboration(HRC)rather than on enhancing collaboration efficiency.To improve the overall collaboration efficiency while maintaining a high success rate,this study proposes an active interaction strategy generation for HRC based on trust.First,a trust-based optimal robot strategy generation method was proposed to generate the robot’s optimal strategy in a HRC.This method employs a tree to model the HRC process under different robot strategies and calculates the optimal strategy based on the modeling results for the robot to execute.Second,the robot’s performance was evaluated to calculate human’s trust in a robot.A robot performance evaluation method based on a visual language model was also proposed.The evaluation results were input into the trust model to compute human’s current trust.Finally,each time an object operation was completed,the robot’s performance evaluation and optimal strategy generation methods worked together to automatically generate the optimal strategy of the robot for the next step until the entire collaborative task was completed.The experimental results demonstrates that this method significantly improve collaborative efficiency,achieving a high success rate in HRC.
