Tracking and following pedestrian trajectories, an approach for autonomous surveillance of critical infrastructures. Issue 5 (17th August 2015)

• Record Type: Journal Article
• Authors: Garzon Oviedo, Mario Andrei; Barrientos, Antonio; Del Cerro, Jaime; Alacid, Andrés; Fotiadis, Efstathios; Rodríguez-Canosa, Gonzalo R.; Wang, Bang-Chen
• Is Part Of: Industrial robot. Volume 42:Issue 5(2015)
• Page Start: 429
• Page End: 440
• Publication Date: 2015-08-17
• Subjects: Navigation -- Autonomous robots -- Image processing -- Field robotics; Robots, Industrial -- Periodicals; Machinery in the workplace -- Periodicals; 629.892
• DOI: 10.1108/IR-02-2015-0037
• Languages: English
• ISSNs: 0143-991X

Abstract

Abstract : Purpose: – This paper aims to present a system that is fully capable of addressing the issue of detection, tracking and following pedestrians, which is a very challenging task, especially when it is considered for using in large outdoors infrastructures. Three modules, detection, tracking and following, are integrated and tested over long distances in semi-structured scenarios, where static or dynamic obstacles, including other pedestrians, can be found. Design/methodology/approach: – The detection is based on the probabilistic fusion of a laser scanner and a camera. The tracking module pairs observations with previously detected targets by using Kalman Filters and a Mahalanobis-distance. The following module allows to safely pursue the target by using a well-defined navigation scheme. Findings: – The system can track pedestrians from static position to 3.46 m/s (running). It handles occlusions, crossings or miss-detections, keeping track of the position even if the pedestrian is only detected in 55/per cent of the observations. Moreover, it autonomously selects and follows a target at a maximum speed of 1.46 m/s. Originality/value: – The main novelty of this study is the integration of the three algorithms in a fully operational system, tested in real outdoor scenarios. Furthermore, the addition of labelling to the detection algorithm allows using the full range of a single sensor while preserving the high performance of a combined detection. False-positives' rate is reduced by handling the uncertainty level when pairing observations. The inclusion of pedestrian speed in the model speeds up and simplifies tracking process. Finally, the most suitable target is automatically selected by a scoring system.