Foreground object detection in highly dynamic scenes using saliency

In this paper, we propose a novel saliency-based algorithm to detect foreground regions in highly dynamic scenes. We first convert input video frames to multiple patch-based feature maps. Then, we apply temporal saliency analysis to the pixels of each feature map. For each temporal set of co-located pixels, the feature distance of a point from its k^th nearest neighbor is used to compute the temporal saliency. By computing and combining temporal saliency maps of different features, we obtain foreground likelihood maps. A simple segmentation method based on adaptive thresholding is applied to detect the foreground objects. We test our algorithm on images sequences of dynamic scenes, including public datasets and a new challenging wildlife dataset we constructed. The experimental results demonstrate the proposed algorithm achieves state-of-the-art results.