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The density-based spatial clustering of application with noise (DBSCAN) algorithm has good robustness and is widely employed to cluster vehicle trajectories for vehicle movement pattern recognition. However, the distance or similarity between two trajectories varies from tens to hundreds of thousands, and there is no effective method for determining the values of the hyperparameters eps and MinPts of DBSCAN. In addition, with increasing sizes of trajectory datasets, some trajectory clustering methods that directly analyse points and line segments incur large computational costs and time overhead. To solve these two dilemmas, the authors propose an effective trajectory dimensionality reduction method and a DBSCAN hyperparameter initial value setting method. The trajectory dimensionality reduction algorithm processes trajectories with different lengths into the same dimensionality (the same number of feature points). The reserved points preserve the spatial and temporal information of these trajectories as much as possible. The DBSCAN hyperparameter initial value setting algorithm obtains the effective initial values of eps and MinPts for facilitating subsequent adjustments. Finally, we validate these proposed methods on two trajectory datasets collected from two real-world scenes, and the experimental results are promising and effective.

This paper proposes a new low‐light image enhancement method, which we call the Local Discrete Mapping Method. The new method limits the processing range to small areas with a high information relevance, which can better coordinate the enhancement quality of each area. First, the discrete mapping relationship of pixels (called discrete mapping points) globally occupying a small part of the critical gray value was extracted and designated to keep the enhancement amplitude of each local area consistent. Then, other free mapping points were adjusted according to the local features to achieve the best visual effect in each local area. In addition, this paper also proposes a span correction function that takes the gray span between local pixels as the adjustment object. The function can preserve the gray difference between freely mapped pixels to the maximum and significantly reduce detailed damage in the local area. Finally, we used 1500 test images and eleven objective evaluation indicators in the public dataset to comprehensively test the seven methods. The experimental results showed that the proposed method has an excellent dark area quality enhancement, brightness detail protection, overall noise suppression, and processing speed. It is significantly better than similar methods in terms of visual quality and quantitative testing.