Detecting moving people in video streams

The detection of moving people is an important task for video surveillance systems. This paper presents a motion segmentation algorithm for detecting people moving in indoor environments. The proposed algorithm works with mobile cameras and it is composed of two main parts. In the first part, a frame-by-frame procedure is applied to compute the difference image, and a neural network is used to classify whether the resulting image represents a static scene or a scene containing mobile objects. The second part tries to reduce the detection errors in terms of both false or missed alarms. A finite state automaton has been designed to give a robust classification and to reduce the number of false or missed blobs. Finally, a bounding ellipse is computed for each detected blob in order to isolate moving people.