The smoothness of high-speed railway tracks is an important indicator for judging the quality of railway tracks. Accurately predicting the uneven trend of the track and dealing with it in advance is of great significance to the safe operation of high-speed railways. According to the data characteristics of specific road sections, this paper proposes a SSA-LSTM model based on dual-stage decomposition to predict track irregularity of high-speed railways. First, in order to eliminate the influence of noise in the original data, Singular Spectrum Analysis (SSA) is used to decompose, denoise, and reconstruct the original data. Secondly, in view of the existence of multiple modal mixing effects in the denoised data, the data is decomposed through the ensemble empirical mode decomposition (EEMD) method. Then, to solve the problem that the long short-term memory network (LSTM) hyper-parameters are difficult to effectively determine, the Sparrow Search Algorithm (SSA) is used to search and optimize. Finally, the optimized LSTM model is used to predict and reintegrate the decomposed multiple sequences. Experimental results show that the SSA-LSTM prediction model based on dual-stage decomposition proposed in this article can obtain more accurate prediction results.
Various mainstream target tracking algorithms based on siamese networks are gradually becoming a trend in the field of deep learning tracking due to their concurrent advantages of accuracy and speed. Most siamese network-based trackers describe the tracking of a target object as a similar matching problem, and these trackers have achieved more advanced performance in several public tests. Most trackers often suffer from tracking drift or performance degradation owing to the non-updating of the template in the first frame and the target appearance encounters disturbing environments such as occlusion and drastic deformation. To address the current problem, this paper proposes an algorithm for fusion of multi-branch modules based on siamese network to achieve the fusion of target templates for updating, which improves the tracker's anti-jamming ability and the network structure is anchor-free. Meanwhile, this paper designs a new fusion module that fuses templates by inserting weight tensor method for multi-template fusion and optimises the results by complementary weight tensors. This method is practiced in SiamFC++ algorithm, where the target dataset is input and features are extracted, and then classification and regression operations are performed by fusion of multi-branch modules to get the predicted position of the target.
In this paper, we describe a novel method for visual vehicle tracking process based on the combination of speeded-up robust features (SURF) points and color feature. The whole tracking process is constructed in the framework of particle filter. To further improve the precision and stability of tracking, a dynamic update mechanism of target template is proposed to capture appearance changes. This mechanism includes two strategies: Adopting new feature points and discarding bad feature points. A novel distance kernel function method is adopted to allocate the weight of each particle, and to improve the stability of the tracking template. The experiments present that our algorithm can track the targets more robustly and adaptively than the traditional algorithms.
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