Research Of Abandoned Objects Detection Based On Bayesian Background Modeling

In computer vision and video surveillance field, background modeling is a key technology. The higher level projects such as moving objects detection,classification,tracking and behavior understanding depend on the results of background modeling. Background modeling is one of the fundamental parts for video analysis, and which is currently a hot topic widely researched around the world. This paper focuses on the background modeling of complex scenes, and places emphasis on Bayesian background estimation algorithm. The main contributions of this paper are summarized as follows:First, Gaussian mixture model(GMM) is used to model background images which are static and dynamic scenes, and through the analysis of experiment results, we conclude that GMM can get a good effect only when the background is static or partly dynamic;however,while dealing with complex outdoor scenes, results are not nice.Then, Bayesian network algorithm is introduced to improve GMM in this paper,and improvements are proposed for this method. Firstly, for convergence problem of this model, the EM algorithm is applied to ensure fast convergence of the algorithm. Secondly, a thresholding method based on sample mean and standard deviation is presented, which can class pixels more accurately. At last, some measures have been taken in image post-processing such as noise suppression and Mathematical Morphology technology to remove noise and increase connectivity.Finally, this model is applied to detect abandoned and removed objects. By processing the input video at different frame rates, two backgrounds are constructed: one for short-term and another for long-term. Two binary foreground maps are estimated by comparing the current frame with the backgrounds, and motion statistics are aggregated in a likelihood image by applying a set of foreground maps. Likelihood image is then used to differentiate between the pixels that belong to moving objects, temporarily static regions and scene background. The temporary static regions indicate abandoned items, illegally parked vehicles, objects removed from the scene. And this presented pixel-wise method does not require object tracking and can be performed easily.The experimental results show that the approach proposed in this thesis is effective and reliable.