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Saliency-based methods have been widely used in the fusion of infrared (IR) and visible (VIS) images, which can highlight the salient object region and preserve the detailed background information simultaneously. However, most existing methods ignore the salient information in the VIS image or they fail to highlight the boundaries of objects, which makes the final saliency map incomplete and the edges of the object blurred. To address the above-mentioned issues, we propose a novel IR and VIS images' fusion algorithm based on the Poisson reconstruction and saliency detection using the Dempster-Shafer (DS) theory. In detail, we mix the gradient using a mask map derived from the saliency map, which could avoid low contrast and halo effects in the results. Besides, both the intensity saliency of the IR image and the structural saliency of all source images are considered by DS to suppress some noise in the IR image. Thus, we could obtain smooth object contours and enhance the edge information of the salient region. Moreover, we also propose a novel probability mass function to calculate the probabilistic map in the process of applying DS to decrease the error from manually assigning the prior probability. Finally, the extensive qualitative and quantitative experiments have demonstrated the advantages and effectiveness of our method compared with other nine state-of-the-art IR and VIS image fusion methods.

The accurate and rapid prediction of parking availability is helpful for improving parking efficiency and to optimize traffic systems. However, previous studies have suffered from limited training sample sizes and a lack of thorough investigation into the correlations among the factors affecting parking availability. The purpose of this study is to explore a prediction method that can account for multiple factors. Firstly, a dynamic prediction method based on a temporal convolutional network (TCN) model was confirmed to be efficient for ultra-short-term parking availability with an accuracy of 0.96 MSE. Then, an attention-enhanced TCN (A-TCN) model based on spatial attention modules was proposed. This model integrates multiple factors, including related dates, extreme weather, and human control, to predict the daily congestion index of parking lots in the short term, with a prediction period of up to one month. Experimental results on real data demonstrate that the MSE of A-TCN is 0.0061, exhibiting better training efficiency and prediction accuracy than a traditional TCN for the short-term prediction time scale.

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