# Track Line Recognition Based on Morphological Thinning Algorithm +
## 2 Related Work  
In the field of intelligent driving of trains, determining the track line in front of the train is an important issue in autopilot technology. To detect track lines, it is convenient to divide the track area, reduce the obstacle detection range, and improve the calculation speed and accuracy of target recognition. Currently, there are many methods and systems to help drivers drive safely [14; 15]. With the development of intelligent train research technology and computer vision, new technologies are increasingly applied to track line detection ahead of trains. Researchers such as [PERSON] and [PERSON] [16] proposed a vision-based track extraction algorithm using dynamic programming. They used dynamic programming to calculate the optimal path: this extracted rail space with minimal cost, and then used dynamic programming to extract left and right rails at the same time. [PERSON] et al. [17] proposed a simple and effective geometric constraint, which was derived from the unique properties of the rail, in order to achieve fast and reliable track detection. [PERSON], [PERSON] et al. [18] introduced a method to perform rail extraction by matching edge features with candidate rail patterns modeled as parabolic segment sequences. [PERSON] et al. [19] and others proposed a track recognition algorithm based on HOG features. Firstly, the image was divided into a series of cells, then the cell located at the bottom of the image was taken as the seed point, and the seed region growth was carried out by comparing the similarity of HOG features of adjacent cells. Finally, the track was extracted from the seed region growth results. In [20], a cost-effective visual turnout detection method was proposed. [PERSON] [21] and other researchers first established a curvature map and a direction map according to camera parameters, then calculated the most likely track curvature according to the edge information of the image, obtained the initial mask of the near track from the curvature, and then corrected the initial mask by the learning method to obtain the near track. [PERSON] et al. [22] first set up a sliding window at the starting position of the track. The point with the largest gradient change in the window was regarded as the characteristic point of the track, and the window slid upward along the gradient direction to continue searching for the characteristic point of the track. In [23], the cumulative value of pixel gradient was first calculated in blocks and the bottom track feature points were determined by combining prior knowledge. A method based on angle alignment measurement was proposed to extract the track feature points upward. Work by [24] implemented the recursive estimation method to extract track lines. [PERSON] [25] and other scholars used a heuristic algorithm to detect rails and sleepers with high accuracy; the algorithm then processed the image through Gaussian filtering and edge detection. However, the detection distance of this heuristic algorithm was extremely limited. In [26], an automatic rail detection method was proposed; it used Hu moment invariant features to realize track line searches and B-spline curves as fitting models. It was necessary to fit the near and far view regions, respectively. The various methods proposed in the above research achieved certain effects for specific scenes. In reference [27], by moving the three-dimensional laser scanning technology, a region growth-fitting algorithm was realized by using the tracking points with clear intensity filtering and the strategy of K-means clustering fusion, so as to extract the vector line of the railway track, and then further distinguish the situations of bends and turnouts. Compared with high-speed railway tracks, tram tracks [28] and subway tracks, however, the appearance of gravel roads, irregular sleepers, road junctions and ravine fences make the surrounding environment more complex; furthermore, there are curves with larger curvatures and complex turnout conditions.