The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 45–51, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-45-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 45–51, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-45-2022
 
30 May 2022
30 May 2022

REAL-TIME FOREGROUND SEGMENTATION FOR SURVEILLANCE APPLICATIONS IN NRCS LIDAR SEQUENCES

L. Kovács1,2, M. Kégl1, and C. Benedek1,2 L. Kovács et al.
  • 1Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network, 1111 Budapest, Kende utca 13-17, Hungary
  • 2Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, 1083 Budapest, Práter utca 50/A, Hungary

Keywords: Lidar, non-repetitive circular scanning, foreground segmentation, background model, surveillance

Abstract. In this paper, we propose a point-level foreground-background separation technique for the segmentation of measurement sequences of a Non-repetitive Circular Scanning (NRCS) Lidar sensor, which is used as a 3D surveillance camera mounted in a fixed position. We show that by applying the NRCS Lidar technology, we can overcome various limitations of rotating multi-beam Lidar sensors, such as low vertical measurement resolution, which is disadvantageous in surveillance applications. As the main challenge, we need to efficiently balance between the spatial and the temporal resolution of the recorded range data. For this reason, we automatically generate and maintain a very high-resolution background model of the sensor’s Field of View, while for enabling real-time analysis of dynamic objects we use low integration time to extract the consecutive time frames. As a result, the laser reflections from foreground objects reflect sparse, but geometrically accurate samples of the silhouettes providing valuable input for higher-level shape description or event analysis steps. We demonstrate the efficiency of the new approach in different realistic NRCS Lidar measurements sequences, obtaining a 0.76 overall F1-score on the measured dataset.