Successive Texture and Shape based Active Contours for Train Bogie Part Segmentation in Rolling Stock Videos

Abstract

Train Rolling Stock Examination (TRSE) is a pro-cedure for checking damages in the undercarriage of a moving train at 30kmph. The undercarriage of a train is called bogie according to railway manuals. Traditionally, TRSE is performed manually by set of highly skilled personnel of the railway near to the train stations. This paper presents a new method to segment the TRSE bogie parts which can assist trained railway personnel for better performance and consequently reduce train accidents. This work uses visualization techniques as a pair of virtual eyes to help checking of each bogie part remotely using high speed video data. Our previous AC models are being supervised by a weak shape image which has shown to improve segmentation accuracies on a closely packed inhomogeneous train bogie object space. However, the inner texture of the objects in the bogies is found to be necessary for better object segmentation. Here, this paper proposes an algorithm for bogie parts segmentation as successive texture and shape-based AC model (STSAC). In this direction, texture of the bogie part is applied serially before the shape to guide the contour towards the desired object of interest. This contrasts with the previous approaches where texture is applied to extract object shape, loosing texture information completely in the output image. To test the proposed method for their ability in extracting objects from videos captured under ambient conditions, the train rolling stock video database is built with 5 videos. In contrast to previous models the proposed method has produced shape rich texture objects through contour evolution performed sequentially.