The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1177–1183, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1177-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1177–1183, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1177-2019

  05 Jun 2019

05 Jun 2019

IMAGE-BASED VEHICLE TRACKING FROM ROADSIDE LIDAR DATA

J. Zhang1, W. Xiao1, B. Coifman2, and J. P. Mills1 J. Zhang et al.
  • 1School of Engineering, Newcastle University, Newcastle upon Tyne, UK
  • 2Department of Civil, Environmental and Geodetic Engineering / Department of Electrical and Computer Engineering, The Ohio State University, Columbus, USA

Keywords: image registration, lidar, template matching, vehicle detection, vehicle tracking

Abstract. Vehicle tracking is of great importance in urban traffic systems, and the adoption of lidar technologies – including on-board and roadside systems – has significant potential for such applications. This research therefore proposes and develops an image-based vehicle-tracking framework from roadside lidar data to track the precise location and speed of a vehicle. Prior to tracking, vehicles are detected in point clouds through a three-step procedure. Cluster tracking then provides initial tracking results. The second tracking stage aims to provide more precise results, in which two strategies are developed and tested: frame-by-frame and model-matching strategies. For each strategy, tracking is implemented through two threads by converting the 3D point cloud clusters into 2D images relating to the plan and side views along the tracked vehicle’s trajectory. During this process, image registration is exploited in order to retrieve the transformation parameters between every image pair. Based on these transformations, vehicle speeds are determined directly based on (a) the locations of the chosen tracking point in the first strategy; (b) a vehicle model is built and tracking point locations can be calculated after matching every frame with the model in the second strategy. In contrast with other existing methods, the proposed method provides improved vehicle tracking via points instead of clusters. Moreover, tracking in a decomposed manner provides an opportunity to cross-validate the results from different views. The effectiveness of this method has been evaluated using roadside lidar data obtained by a Robosense 32-line laser scanner.