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
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.
