Compared to 3D object detection using a single camera,multiple cameras can overcome some limitations on field-of-view,occlusion,and low detection confidence.This study employs multiple surveillance cameras and develop...Compared to 3D object detection using a single camera,multiple cameras can overcome some limitations on field-of-view,occlusion,and low detection confidence.This study employs multiple surveillance cameras and develops a cooperative 3D object detection and tracking framework by incorporating temporal and spatial information.The framework consists of a 3D vehicle detection model,cooperatively spatial-temporal relation scheme,and heuristic camera constellation method.Specifically,the proposed cross-camera association scheme combines the geometric relationship between multiple cameras and objects in corresponding detections.The spatial-temporal method is designed to associate vehicles between different points of view at a single timestamp and fulfill vehicle tracking in the time aspect.The proposed framework is evaluated based on a synthetic cooperative dataset and shows high reliability,where the cooperative perception can recall more than 66%of the trajectory instead of 11%for single-point sensing.This could contribute to full-range surveillance for intelligent transportation systems.展开更多
Social perception refers to how individuals interpret and understand the social world.It is a foundational area of theory and measurement within the social sciences,particularly in communication,political science,psyc...Social perception refers to how individuals interpret and understand the social world.It is a foundational area of theory and measurement within the social sciences,particularly in communication,political science,psychology,and sociology.Classical models include the Stereotype Content Model(SCM),Dual Perspective Model(DPM),and Semantic Differential(SD).Extensive research has been conducted on these models.However,their interrelationships are still difficult to define using conventional comparison methods,which often lack efficiency,validity,and scalability.To tackle this challenge,we employ a text-based computational approach to quantitatively represent each theoretical dimension of the models.Specifically,we map key content dimensions into a shared semantic space using word embeddings and automate the selection of over 500 contrasting word pairs based on semantic differential theory.The results suggest that social perception can be organized around two fundamental components:subjective evaluation(e.g.,how good or likable someone is)and objective attributes(e.g.,power or competence).Furthermore,we validate this computational approach with the widely used Rosenberg’s 64 personality traits,demonstrating improvements in predictive performance over previous methods,with increases of 19%,13%,and 4%for the SD,DPM,and SCM dimensions,respectively.By enabling scalable and interpretable comparisons across these models,our findings would facilitate both theoretical integration and practical applications.展开更多
Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-...Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.展开更多
基金the National Natural Science Foundation of China(No.61873167)the Automotive Industry Science and Technology Development Foundation of Shanghai(No.1904)。
文摘Compared to 3D object detection using a single camera,multiple cameras can overcome some limitations on field-of-view,occlusion,and low detection confidence.This study employs multiple surveillance cameras and develops a cooperative 3D object detection and tracking framework by incorporating temporal and spatial information.The framework consists of a 3D vehicle detection model,cooperatively spatial-temporal relation scheme,and heuristic camera constellation method.Specifically,the proposed cross-camera association scheme combines the geometric relationship between multiple cameras and objects in corresponding detections.The spatial-temporal method is designed to associate vehicles between different points of view at a single timestamp and fulfill vehicle tracking in the time aspect.The proposed framework is evaluated based on a synthetic cooperative dataset and shows high reliability,where the cooperative perception can recall more than 66%of the trajectory instead of 11%for single-point sensing.This could contribute to full-range surveillance for intelligent transportation systems.
文摘Social perception refers to how individuals interpret and understand the social world.It is a foundational area of theory and measurement within the social sciences,particularly in communication,political science,psychology,and sociology.Classical models include the Stereotype Content Model(SCM),Dual Perspective Model(DPM),and Semantic Differential(SD).Extensive research has been conducted on these models.However,their interrelationships are still difficult to define using conventional comparison methods,which often lack efficiency,validity,and scalability.To tackle this challenge,we employ a text-based computational approach to quantitatively represent each theoretical dimension of the models.Specifically,we map key content dimensions into a shared semantic space using word embeddings and automate the selection of over 500 contrasting word pairs based on semantic differential theory.The results suggest that social perception can be organized around two fundamental components:subjective evaluation(e.g.,how good or likable someone is)and objective attributes(e.g.,power or competence).Furthermore,we validate this computational approach with the widely used Rosenberg’s 64 personality traits,demonstrating improvements in predictive performance over previous methods,with increases of 19%,13%,and 4%for the SD,DPM,and SCM dimensions,respectively.By enabling scalable and interpretable comparisons across these models,our findings would facilitate both theoretical integration and practical applications.
基金co-supported by the National Key Research and Development Program of China(No. 2021YFB3301504)the National Natural Science Foundation of China (Nos. 62072415, 62036010, 42301526, 62372416 and 62472389)the National Natural Science Foundation of Henan Province, China (No. 242300421215)
文摘Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.
