Vanishing point detection for CCTV in railway stations

Abstract

Vanishing point (VP) detection is an important task in computer vision with particular importance in video surveillance. However, despite having numerous applications in camera calibration, 3D reconstruction and threat detection, a general method for VP detection has remained elusive. Rather than attempting the infeasible task of VP detection in general scenes, this paper presents a novel method that is fine-tuned to work for railway station environments and is shown to outperform the state-of-the-art for that particular case. The motivation for this is that (a) these environments are particularly susceptible to many types of crime from petty theft to terrorist activity, (b) the number of objects/structures in the scenes are limited, rendering the problem more tractable than the generic case, and (c) they typically have many CCTV cameras already installed. The method presented here commences by extracting edges from the input frame using the Canny edge detector as a pre-processing stage before the standard Hough transform is employed. A novel line clustering algorithm is then applied to determine the parameters of the lines that converge at VPs. This is based on statistics of the detected lines and heuristics about the type of scene. The clustered lines are then used to compute VPs using their intersection points. A voting system is used to optimise detection in an attempt to omit spurious lines. The paper includes a direct comparison to the state-of-the-art and ground truth.