Abnormal behavior-detection using sequential syntactical classification in a network of clustered cameras
Rachel Goshorn, Deborah Goshorn, Joshua Goshorn, Lawrence A. Goshorn · 2008
Detecting abnormal behaviors is a critical task today. We need to monitor large areas, manage camera sensor data, and use this data for detecting behaviors, detecting the abnormal behaviors and classifying the normal behaviors. In order to monitor large areas, we need multiple cameras across a large-scale network. We use an architecture for a network of clustered cameras to minimize and efficiently manage bandwidth utilization. From this camera network architecture, we use the infrastructure outputs per cluster, per person, to detect abnormal behaviors intra-cluster; we also use the architecture outputs per person, per network, to detect global (inter-cluster) abnormal behaviors.