Vanishing point detection for visual surveillance systems in railway platform environments

Abstract

Visual surveillance is of paramount importance in public spaces and especially in train and metro platforms which are particularly susceptible to many types of crime from petty theft to terrorist activity. Image resolution of visual surveillance systems is limited by a trade-off between several requirements such as sensor and lens cost, transmission bandwidth and storage space. When image quality cannot be improved using high-resolution sensors, high-end lenses or IR illumination, the visual surveillance system may need to increase the resolving power of the images by software to provide accurate outputs such as, in our case, vanishing points (VPs). 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. In this paper, we propose a three-stage approach to accurately detect the main lines and vanishing points in low-resolution images acquired by visual surveillance systems in indoor and outdoor railway platform environments. First, several frames are used to increase the resolving power through a multi-frame image enhancer. Second, an adaptive edge detection is performed and 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. Finally, vanishing points are computed via a voting system to optimise detection in an attempt to omit spurious lines. The proposed approach is very robust since it is not affected by ever-changing illumination and weather conditions of the scene, and it is immune to vibrations. Accurate and reliable vanishing point detection provides very valuable information, which can be used to aid camera calibration, automatic scene understanding, scene segmentation, semantic classification or augmented reality in platform environments.

A New Camera Calibration Technique for Serious Distortion

“…In addition, a circular area is created with the centre of the image as the centre and 100 pixel points as the radius, and the coordinates of the centroids of all black squares within the circular area are obtained, as shown in Figure 4b. (4) The computer is used to find the combination of three points that satisfy in the same line, and to determine all other points on the same line that are far from the center of the image. Using Equation ( 12) to find the furthest ideal image coordinate points from the center without distortion, the image points found are shown in Figure 4b as A 14 , A 24 , A 34 and A 44 .…”

“…Camera calibration is a necessary step in photogrammetry and computer vision, which plays an important role in many spheres such as 3D-measurement [1], 3D object reconstruction [2], robot navigation [3], visual surveillance [4], and industrial inspection [5]. Many studies have been conducted in this respect.…”

Vanishing Point Detection and Rail Segmentation Based on Deep Multi-Task Learning

“…The proposed VP detection method is also compared with single regression network (SRN) [24], dominate VP detec- tion network (DON) [7], region prediction network (RPN) [23] and Hough transform method (HFT) [19]. The SRN used similar residual network as ours for feature extraction.…”

“…Then, road lines were extracted with several own-defined constraints and VP was estimated through mean-shift clustering. VP detection on railway platform scene was first studied in [19]. Main straight lines were detected by Canny edge detector and Hough transform.…”

An Improved Phase Correlation Method for Stop Detection of Autonomous Driving

“…The intersection point, maybe at infinity, is known as the VP. Because the VP analysis contains the direction information of the straight lines and provides crucial cues for inferring the 3-dimensional geometric structure of a scene, locating the VP correctly has been an active research problem in the field of computer vision [25].…”