Fast video object detection via multiple background modeling

In this paper, a robust background extraction and novel object detection are proposed, which comprise of filtering operations to detect non background objects in a monitoring scene. Conventionally, a statistical background model is extracted by using a training sequence without foreground objects and the background model parameters are being updated continuously to adapt changes in the scene. However, it is not possible to require a monitoring scene to be static. Furthermore, static objects in the scene could be adapted into the background. Problems arise when static objects start to move again. The convention method would produce false alarms in the detection process. In our proposed algorithm, two background models are constructed by using N-bins histogram method to indicate short term and long term changes of the monitoring scene. We then apply background subtractions to the current frame to obtain two error frames, which are combined for objects detection and classification. Extensive experimental work has been done, results of which show that the present approach provides a better solution compared with the conventional approach, including to resolve the problem of re-active objects.