**6. Conclusions**

The proposed algorithm uses an adaptive feature distribution extractor for railway track segmentation by making full use of the strong linear characteristics of railway scenes and the typical categories of the local areas. A good balance between segmentation precision, recognition accuracy, calculation time, and complexity of manual operation can be achieved. By using the proposed algorithm, the railway intrusion detection system can automatically and accurately delimit the boundaries of a surveillance scene in real time and greatly improve the efficiency of the system operation. Considering the fact that, in China, there are over 29,000 km of high-speed railways and the average density of cameras on high-speed railway lines is about 2.92 cameras/km, the proposed algorithm is of grea<sup>t</sup> significance to improve the efficiency.

The proposed algorithm can be applied into the surveillance system of public places such as airport aprons, highway pavement, and squares. These places share some common characteristics: simple structure full of straight lines—such as airplane runways and different functional areas, vehicles and different lanes, pedestrians and sidewalk lines. Before applying this method, however, the training dataset of the simplified CNN has to include new categories in such scenes, then the proposed algorithm can segmen<sup>t</sup> the scene and label each local area.

**Author Contributions:** Conceptualization, Y.W., L.Z., and Z.Y.; Investigation, Y.W. and B.G.; Methodology, Y.W. and L.Z.; Project administration, Z.Y.; Software, Y.W.; Validation, Y.W.; Writing—original draft, Y.W.; Writing—review & editing, Y.W. and L.Z.

**Funding:** This research was funded by National Key Research and Development Program of China (2016YFB1200401).

**Conflicts of Interest:** The authors declare no conflict of interest.
