OCULUS surveillance system: Fuzzy on-line speed analysis from 2D images

This paper presents an independent component integrated into a global surveillance system named as OCULUS. The aim of this component is to classify the speed of moving objects as normal or abnormal in order to detect anomalous events, taking into account the object class and spatio-temporal information such as locations and movements. The proposed component analyses the speed of the detected objects in real-time without needing several cameras, a 3D representation of the environment, or the estimation of precise values. Unlike other works, the proposed method does require knowing the camera parameters previously (e.g. height, angle, zoom level, etc.). The knowledge used by this component is automatically acquired by means of a learning algorithm that generates a set of highly interpretable fuzzy rules. The experimental results demonstrate that the proposed method is accurate, robust and provides a real-time analysis.

► This paper presents a novel method to classify the speed of moving objects in order to detect anomalous video events. ► Our proposal works in real-time without needing several cameras, a 3D representation, or the estimation of precise values. ► The knowledge used by the surveillance component is acquired by means of a learning algorithm that generates a set of highly interpretable fuzzy rules. ► The experimental results demonstrate that the proposed method is accurate, robust and provides a real-time analysis.