1. A computer-implemented method for applying computer vision techniques to automatically detect and classify the presence or absence of a train within a railway track area, the method comprising executing on a processor the steps of: classifying a segment of input video image data comprising a group of consecutive video frames that each comprise “train absent” label and “static” labels as a “no train present” segment, wherein the input video image data is acquired from a region of interest defined around a train track area within an image scene of a stream of the video image data that comprises a portion of a railway track area; classifying a segment of the input video data comprising a group of consecutive video frames that each comprise the “train present” label and a “motion present” label as a “train present and in transition” segment; classifying a segment of the input video data comprising a group of consecutive video frames that each comprise the “train present” label and the “static” label as a “train present and stopped” segment; determining a presence or a motion state of a train within the region of interest of the video scene at a time of inquiry as the “no train present”, the “train present and in transition” or the “train present and stopped” classification of the segment comprising the video frame of the time of inquiry; determining a distribution of the “train present” and “train absent” labels of the plurality of the video segments within an auto-correction time period; in response to the determined distribution of the “train present” and “train absent” labels not meeting an expected distribution for the auto-correction time period, exchanging the “train present” and the “train absent” labels that are determined for each of the video frames; and repeating, as a function of the exchanged “train present” and “train absent” labels, the steps of classifying the segments of the input video data of consecutive video frames that each comprise the “train absent” label and the “static” label as the “no train present” segment groups, classifying the segments of the input video data of consecutive video frames that each comprise the “train present” label and the “motion present” label as the “train present and in transition” segments, and classifying the segments of the input video data of consecutive video frames that each comprise the “train present” label and the “static” label as the “train present and stopped” segment groups.