# Motion Control System of Unmanned Railars Based on Image Recognition
The idea is first to select four predefined signs of different colors and shapes serving as basic motion commands of acceleration, deceleration, reverse and stop for the railcar motion control system. Directly working with only image intensities (i.e., the RGB pixel values (red, green and blue light are added together in various ways to reproduce a broad array of colors) of image) made the task of feature calculation time consuming and computationally expensive. In our application, the railcar must be able to identify four predefined signs while it is moving, and the applied image recognition algorithm should be fast and accurate enough. [PERSON] and [PERSON] developed a very successful fast face detection system [10]. In that system, they prominently used integral images and adapted the idea of using Haar wavelets to develop the so-called Haar-like features to train strength classifiers based on AdaBoosting and Cascade algorithms for detecting faces. [PERSON] et al. [11] further proposed an extended set of Haar-like features to get further improvement. [PERSON] and [PERSON] stronger classifier often outperforms most'monolithic' strong classifiers, such as support vector machines and Neural Networks [12]. Image recognition based on AdaBoosting and Cascade algorithms were applied in autonomous vehicle applications [13; 14; 15; 16]. In those applications, other than camera, Light Detection and Ranging (LIDAR) [13], Global Positioning System (GPS) [14; 15; 16], and road surface maker (RSM) [15; 16] databases in the cloud are also used. In our application, other than a camera, we do not need additional sensing devices. In this research, [PERSON] and [PERSON] AdaBoosting and Cascade algorithms [10] together with [PERSON]'s extended set of Haar-like features [11] are adopted to train the four predefined signs. The control system is pre-trained and is able to recognize those predefined signs in consecutive image frames. We then can properly distribute those predefined signs along the tracksides according to kinematics pre-analyses. When the onboard camera of the railcar transfers continuous image frames to the host computer, the computer can detect those predefined signs and respond properly to control the motion of the railcars. In addition, if we select a trained predefined sign as a positioning sign, we can distribute them with equal distance along the trackside, dividing the distance by the time difference of two consecutive positioning sign detections, and as such, the system can get the estimation of railcar speed just like the CBTC system. With this concept, we constructed an image recognition based motion control system of railcars with extremely low maintenance and immunity against electromagnetic interference in this research. This system can also serve as an auxiliary speed measurement system for the CBTC system, which easily suffers from electromagnetic interference. Open Source Computer Vision Library (OpenCV) [17] is a library for real time computer vision provided by Intel, and OpenCV utilities are properly applied in developing the integrated programs for the system. The operations of the proposed motion control system of railcars are as follows. The on board camera of the railcar serves as the sensor. It transfers continuous image frames through a wireless microwave receiver to the host computer. This host computer identifies the four predefined signs and then generates the proper pulse width modulation (PWM) commands [18] to control the motions of the railcar. The whole control system is compact, low cost and easy to maintain. The railcars used in this experiment consist of a powered car and a camera on board the car. They are DC voltage controlled mini train models with scale of \(1/150\)[19]. When more predefined signs are trained, the experiment setup can be modified to accomplish more complex control tasks for railcars. To the author's best knowledge, no similar railcar motion control system was reported in the literature.