Abnormal Human Activity Recognition and Classification Using Knowledge Graphs

Intelligent verbal and action annotations have played an important role in railway stations, bus stands, police stations and road traffic to avoid crime diffusion, attacks and identify unhealthy human behavior.To predict and classify the human abnormal activities, which is occur at a remote location and/or unusual time by identify the movements from surveillance cameras for identifying the physical and verbal abnormal activities such as abuse, assault, arson, arrest, and fighting and raise an automatic alarm or notification.From the existing gap, an automation tool is required to detect the violence behavior with potential hazards and to prevent from crime to promote public safety.The proposed human activity and annotation recognition (HAAR) algorithm addresses the existing limitations by using contextual semantic analysis for identification of crowd, tracking of crowd behavior in large gatherings and people's abnormal behavior.It uses CNN and LSTM model to categorize the abnormal activity and generate a pattern by using contextual knowledge graph to identify the severity.HAAR model uses contextual knowledge graph with contextual vertices and edges to store human's verbal and physical action and objects involved.The captured attributes and its dependencies between the activities in a knowledge graph recognize the level of unusual behavior and generate ALERT messages to the authorities.