本研究旨在通过引入注意力机制和优化损失函数,实现基于PoseNet模型的手势识别性能的提升。我们选择了MPII Human Pose数据集作为实验平台,该数据集提供了全身姿态估计的信息,通过数据处理将研究焦点集中在手部及其局部特征上,从而实现...本研究旨在通过引入注意力机制和优化损失函数,实现基于PoseNet模型的手势识别性能的提升。我们选择了MPII Human Pose数据集作为实验平台,该数据集提供了全身姿态估计的信息,通过数据处理将研究焦点集中在手部及其局部特征上,从而实现手势识别工作的评估。实验结果显示,在PCK和mAP等评价指标下,改进模型的性能得到了一定的提升;同时,模型在处理复杂环境条件下的稳定性和实时性也得到了增强通过数据分析和实证验证。展开更多
In this paper,we propose a skeleton-based method to identify violence and aggressive behavior.The approach does not necessitate highprocessing equipment and it can be quickly implemented.Our approach consists of two p...In this paper,we propose a skeleton-based method to identify violence and aggressive behavior.The approach does not necessitate highprocessing equipment and it can be quickly implemented.Our approach consists of two phases:feature extraction from image sequences to assess a human posture,followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence.A video violence dataset of 400 min comprising a single person’s activities and 20 h of video data including physical violence and aggressive acts,and 13 classifications for distinguishing aggressor and victim behavior were generated.Finally,the proposed method was trained and tested using the collected dataset.The results indicate the accuracy of 97%was achieved in identifying aggressive conduct in video sequences.Furthermore,the obtained results show that the proposed method can detect aggressive behavior and violence in a short period of time and is accessible for real-world applications.展开更多
文摘本研究旨在通过引入注意力机制和优化损失函数,实现基于PoseNet模型的手势识别性能的提升。我们选择了MPII Human Pose数据集作为实验平台,该数据集提供了全身姿态估计的信息,通过数据处理将研究焦点集中在手部及其局部特征上,从而实现手势识别工作的评估。实验结果显示,在PCK和mAP等评价指标下,改进模型的性能得到了一定的提升;同时,模型在处理复杂环境条件下的稳定性和实时性也得到了增强通过数据分析和实证验证。
基金This work was supported by the grant“Development of artificial intelligenceenabled software solution prototype for automatic detection of potential facts of physical bullying in educational institutions”funded by the Ministry of Education of the Republic of Kazakhstan.Grant No.IRN AP08855520.
文摘In this paper,we propose a skeleton-based method to identify violence and aggressive behavior.The approach does not necessitate highprocessing equipment and it can be quickly implemented.Our approach consists of two phases:feature extraction from image sequences to assess a human posture,followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence.A video violence dataset of 400 min comprising a single person’s activities and 20 h of video data including physical violence and aggressive acts,and 13 classifications for distinguishing aggressor and victim behavior were generated.Finally,the proposed method was trained and tested using the collected dataset.The results indicate the accuracy of 97%was achieved in identifying aggressive conduct in video sequences.Furthermore,the obtained results show that the proposed method can detect aggressive behavior and violence in a short period of time and is accessible for real-world applications.
