Abandoned Objects Detection Using Double Illumination Invariant Foreground Masks

This paper proposes an automatic and robust method to detect and recognize the abandoned objects for video surveillance systems. Two Gaussian Mixture Models(Long-term and Short-term models) in the RGB color space are constructed to obtain two binary foreground masks. By refining the foreground masks through Radial Reach Filter (RRF) method, the influence of illumination changes is greatly reduced. The height/width ratio and a linear SVM classifier based on HOG (Histogram of Oriented Gradient) descriptor is also used to recognize the left-baggage. Tests on datasets of PETS2006, PETS2007 and our own videos show that the proposed method in this paper can detect very small abandoned objects within low quality surveillance videos, and it is also robust to the varying illuminations and dynamic background.