In today’s information age,video data,as an important carrier of information,is growing explosively in terms of production volume.The quick and accurate extraction of useful information from massive video data has be...In today’s information age,video data,as an important carrier of information,is growing explosively in terms of production volume.The quick and accurate extraction of useful information from massive video data has become a focus of research in the field of computer vision.AI dynamic recognition technology has become one of the key technologies to address this issue due to its powerful data processing capabilities and intelligent recognition functions.Based on this,this paper first elaborates on the development of intelligent video AI dynamic recognition technology,then proposes several optimization strategies for intelligent video AI dynamic recognition technology,and finally analyzes the performance of intelligent video AI dynamic recognition technology for reference.展开更多
Football is one of the most-watched sports,but analyzing players’per-formance is currently difficult and labor intensive.Performance analysis is done manually,which means that someone must watch video recordings and ...Football is one of the most-watched sports,but analyzing players’per-formance is currently difficult and labor intensive.Performance analysis is done manually,which means that someone must watch video recordings and then log each player’s performance.This includes the number of passes and shots taken by each player,the location of the action,and whether or not the play had a successful outcome.Due to the time-consuming nature of manual analyses,interest in automatic analysis tools is high despite the many interdependent phases involved,such as pitch segmentation,player and ball detection,assigning players to their teams,identifying individual players,activity recognition,etc.This paper proposes a system for developing an automatic video analysis tool for sports.The proposed system is the first to integrate multiple phases,such as segmenting the field,detecting the players and the ball,assigning players to their teams,and iden-tifying players’jersey numbers.In team assignment,this research employed unsu-pervised learning based on convolutional autoencoders(CAEs)to learn discriminative latent representations and minimize the latent embedding distance between the players on the same team while simultaneously maximizing the dis-tance between those on opposing teams.This paper also created a highly accurate approach for the real-time detection of the ball.Furthermore,it also addressed the lack of jersey number datasets by creating a new dataset with more than 6,500 images for numbers ranging from 0 to 99.Since achieving a high perfor-mance in deep learning requires a large training set,and the collected dataset was not enough,this research utilized transfer learning(TL)to first pretrain the jersey number detection model on another large dataset and then fine-tune it on the target dataset to increase the accuracy.To test the proposed system,this paper presents a comprehensive evaluation of its individual stages as well as of the sys-tem as a whole.展开更多
船舶运动模式的提取是轨迹数据分析的重要任务,它可以为船舶异常行为的检测提供参考依据,同时也可以作为航路规划和定线制设计的技术指标.针对现存的聚类算法大多为了追求效率而忽略了运动轨迹特征的问题,对聚类算法中的轨迹结构距离进...船舶运动模式的提取是轨迹数据分析的重要任务,它可以为船舶异常行为的检测提供参考依据,同时也可以作为航路规划和定线制设计的技术指标.针对现存的聚类算法大多为了追求效率而忽略了运动轨迹特征的问题,对聚类算法中的轨迹结构距离进行改进,将其作为轨迹相似度的评价标准.采用无监督DBSCAN聚类算法实现船舶运动模式的提取.利用琼州海峡船舶自动识别系统(Automatic Identification System,AIS)数据,对该水域的船舶运动模式进行提取,获得行驶于该水域的船舶运动轨迹分布以及各类轨迹中转向区域的分布,其中船舶运动轨迹包括从琼州海峡东峡口向西航行的船舶轨迹,从琼州海峡西峡口向东航行的船舶轨迹,从秀英港前往海安港的船舶轨迹,从海安港前往秀英港的船舶轨迹和从琼州海峡东峡口前往海口港的船舶轨迹.将最终的聚类结果应用于电子海图显示与信息系统(Electronic Chart Display and Information System,ECDIS)上,实现了对船舶的动态监控仿真.展开更多
文摘In today’s information age,video data,as an important carrier of information,is growing explosively in terms of production volume.The quick and accurate extraction of useful information from massive video data has become a focus of research in the field of computer vision.AI dynamic recognition technology has become one of the key technologies to address this issue due to its powerful data processing capabilities and intelligent recognition functions.Based on this,this paper first elaborates on the development of intelligent video AI dynamic recognition technology,then proposes several optimization strategies for intelligent video AI dynamic recognition technology,and finally analyzes the performance of intelligent video AI dynamic recognition technology for reference.
文摘Football is one of the most-watched sports,but analyzing players’per-formance is currently difficult and labor intensive.Performance analysis is done manually,which means that someone must watch video recordings and then log each player’s performance.This includes the number of passes and shots taken by each player,the location of the action,and whether or not the play had a successful outcome.Due to the time-consuming nature of manual analyses,interest in automatic analysis tools is high despite the many interdependent phases involved,such as pitch segmentation,player and ball detection,assigning players to their teams,identifying individual players,activity recognition,etc.This paper proposes a system for developing an automatic video analysis tool for sports.The proposed system is the first to integrate multiple phases,such as segmenting the field,detecting the players and the ball,assigning players to their teams,and iden-tifying players’jersey numbers.In team assignment,this research employed unsu-pervised learning based on convolutional autoencoders(CAEs)to learn discriminative latent representations and minimize the latent embedding distance between the players on the same team while simultaneously maximizing the dis-tance between those on opposing teams.This paper also created a highly accurate approach for the real-time detection of the ball.Furthermore,it also addressed the lack of jersey number datasets by creating a new dataset with more than 6,500 images for numbers ranging from 0 to 99.Since achieving a high perfor-mance in deep learning requires a large training set,and the collected dataset was not enough,this research utilized transfer learning(TL)to first pretrain the jersey number detection model on another large dataset and then fine-tune it on the target dataset to increase the accuracy.To test the proposed system,this paper presents a comprehensive evaluation of its individual stages as well as of the sys-tem as a whole.
文摘船舶运动模式的提取是轨迹数据分析的重要任务,它可以为船舶异常行为的检测提供参考依据,同时也可以作为航路规划和定线制设计的技术指标.针对现存的聚类算法大多为了追求效率而忽略了运动轨迹特征的问题,对聚类算法中的轨迹结构距离进行改进,将其作为轨迹相似度的评价标准.采用无监督DBSCAN聚类算法实现船舶运动模式的提取.利用琼州海峡船舶自动识别系统(Automatic Identification System,AIS)数据,对该水域的船舶运动模式进行提取,获得行驶于该水域的船舶运动轨迹分布以及各类轨迹中转向区域的分布,其中船舶运动轨迹包括从琼州海峡东峡口向西航行的船舶轨迹,从琼州海峡西峡口向东航行的船舶轨迹,从秀英港前往海安港的船舶轨迹,从海安港前往秀英港的船舶轨迹和从琼州海峡东峡口前往海口港的船舶轨迹.将最终的聚类结果应用于电子海图显示与信息系统(Electronic Chart Display and Information System,ECDIS)上,实现了对船舶的动态监控仿真.
