The remote and automatic inspection of the inside of pipes and tunnels is an important industrial application area. The main characteristics of the environment found in commonly used pipes such as sewers are: limitations on the camera spatial position; a large variety of surface features; a wide range of surface reflectivity due to the orientation of parts of the pipe, e.g. the joints; and many disturbances to the environment due for example to: mist, water spray, or hanging debris. The objective of this research is defect detection and classification; however, a first stage is the construction of a model of the pipe structure by pipe joint tracking. This paper describes work to exploit the knowledge of the environment to: build a model of the defects, reflectivity characteristics and pipe characteristics; develop appropriate methods for grouping the pipe joint features within each image from edge information; fit a pipe joint model (a circle, or connected arcs) to the grouped features; and to track these features in sequential images. Each stage in these processes has been analyzed to optimize the performance in terms of reliability and speed of operation. The methods that have been developed are described and results of robust pipe joint tracking over a large sequence of images are presented. The paper also presents results of experiments of applying several common edge detectors to images which have been corrupted by JPEG encoding and spatial sub-sampling. The subsequent robustness of a Hough based method for the detection of circular images features is also reported.