Simplified SOM-neural model for video segmentation of moving objects
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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This paper proposes an efficient method for detecting ghost and left objects in surveillance video, which, if not identified, may lead to errors or wasted computation in background modeling and object tracking in surveillance systems. This method contains two main steps: the first one is to detect stationary objects, which narrows down the evaluation targets to a very small number of foreground blobs; the second step is to discriminate the candidates between ghost and left objects. For the first step, we introduce a novel stationary object detection method based on continuous object tracking and shape matching. For the second step, we propose a fast and robust inpainting method to differentiate between ghost and left objects by constructing the real background using the candidate’s corresponding regions in the input and the background images. The effectiveness of our method has been validated by experiments over a variety of video sequences.