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Moving vehicle segmentation in traffic videos is a challenging work because of complex background and variety objects. In this paper, we focus on detecting vehicles that are running through crossroads using the up-to-date spatiotemporal saliency model. The current saliency detection methods aim at detecting the most salient objects, novel but stationary target will be easily classified as foreground,...
This paper proposed a moving vehicles segmentation approach combined two kinds of classifiers which are Gaussian mixture model and spatiotemporal saliency map. We extracted new spatial and temporal saliency features and improved spatiotemporal consistency optimization model to calculate more exact saliency map and to speed up the processing. Because misclassification appears when Gaussian background...
Crowd density estimation is important in crowd analysis, this paper proposes a new approach used for crowd density estimation. First, background is removed by using a combination of optical flow and background subtract methods. Then according to texture analysis, a set of new feature is extracted from foreground image. Finally, a self-organizing map neural network is used for classifying different...
Crowd density estimation is important in crowd analysis and texture analysis is an efficient method to estimate crowd density, this paper proposes an improved estimation approach based on texture analysis. First, background is removed by using a combination of optical flow and background subtract method. Then according to texture analysis, a set of new feature is extracted from foreground image. Finally,...
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