Simultaneous Detection and Tracking of Pedestrian From  Panoramic Laser Scanning Data

Xiao, Wen; Vallet, Bruno; Schindler, Konrad; Paparoditis, Nicolas

doi:10.5194/isprs-annals-iii-3-295-2016

Cited by 15 publications

(3 citation statements)

References 27 publications

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“…(i) Point-Based Method: Xiao et al [9] developed nearest point methods to subtract background points based on the assumptions that background points have more neighbors than moving targets within a time window. Zhang et al [10] developed roadside vehicle detection and tracking method based on ground plane removal.…”

Section: Related Workmentioning

confidence: 99%

Roadside LiDAR Vehicle Detection and Tracking Using Range and Intensity Background Subtraction

Zhang

Jin

2022

Journal of Advanced Transportation

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In this study, we developed the solution of roadside LiDAR object detection using a combination of two unsupervised learning algorithms. The 3D point clouds are firstly converted into spherical coordinates and filled into the elevation-azimuth matrix using a hash function. After that, the raw LiDAR data were rearranged into the new data structure to store the information of range, azimuth, and intensity. Then, the dynamic mode decomposition method is applied to decompose the LiDAR data into low-rank backgrounds and sparse foregrounds based on intensity channel pattern recognition. The coarse-fine triangle algorithm (CFTA) automatically finds the dividing value to separate the moving targets from static background according to range information. After intensity and range background subtraction, the foreground moving objects will be detected using a density-based detector and encoded into the state-space model for tracking. The output of the proposed solution includes vehicle trajectories that can enable many mobility and safety applications. The method was validated at both path and point levels and outperformed the state of the art. In contrast to the previous methods that process directly on the scattered and discrete point clouds, the dynamic classification method can establish the less sophisticated linear relationship of the 3D measurement data, which captures the spatial-temporal structure that we often desire.

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Section: Related Workmentioning

confidence: 99%

Roadside LiDAR Vehicle Detection and Tracking Using Range and Intensity Background Subtraction

Zhang

Jin

2022

Journal of Advanced Transportation

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“…However, these methods is bound to segmentation and data association algorithms. Different from them, Xiao et al [39] simultaneously detect and track pedestrian using motion prior. All these traditional methods only obtain point segments instead of 3D orientated bounding boxes.…”

Section: D Point Cloud Trackingmentioning

confidence: 99%

Learning the Incremental Warp for 3D Vehicle Tracking in LiDAR Point Clouds

Tian

Liu

Li³

et al. 2021

Remote Sensing

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Object tracking from LiDAR point clouds, which are always incomplete, sparse, and unstructured, plays a crucial role in urban navigation. Some existing methods utilize a learned similarity network for locating the target, immensely limiting the advancements in tracking accuracy. In this study, we leveraged a powerful target discriminator and an accurate state estimator to robustly track target objects in challenging point cloud scenarios. Considering the complex nature of estimating the state, we extended the traditional Lucas and Kanade (LK) algorithm to 3D point cloud tracking. Specifically, we propose a state estimation subnetwork that aims to learn the incremental warp for updating the coarse target state. Moreover, to obtain a coarse state, we present a simple yet efficient discrimination subnetwork. It can project 3D shapes into a more discriminatory latent space by integrating the global feature into each point-wise feature. Experiments on KITTI and PandaSet datasets showed that compared with the most advanced of other methods, our proposed method can achieve significant improvements—in particular, up to 13.68% on KITTI.

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“…This paper aims at comparing the pedestrian tracking performance obtained with an UWB system, which is well known to be usable in order to obtain reliable positioning in relatively small areas (e.g. indoors), and with a LiDAR, used as static 3D laser scanner, as also considered in certain recent research works (Zhang et al, 2019, Borgmann et al, 2020, Chen et al, 2019, Xiao et al, 2016, Ozaki et al, 2012, Gidel et al, 2010.…”

Section: Introductionmentioning

confidence: 99%

A Comparison Between Uwb and Laser-Based Pedestrian Tracking

Masiero

Dabove²,

Pietra³

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Despite the availability of GNSS on consumer devices enabled personal navigation for most of the World population in most of the outdoor conditions, the problem of precise pedestrian positioning is still quite challenging when indoors or, more in general, in GNSS-challenging working conditions. Furthermore, the covid-19 pandemic also raised of pedestrian tracking, in any environment, but in particular indoors, where GNSS typically does not ensure sufficient accuracy for checking people distance. Motivated by the mentioned needs, this paper investigates the potential of UWB and LiDAR for pedestrian positioning and tracking. The two methods are compared in an outdoor case study, nevertheless, both are usable indoors as well. The obtained results show that the positioning performance of the LiDAR-based approach overcomes the UWB one, when the pedestrians are not obstructed by other objects in the LiDAR view. Nevertheless, the presence of obstructions causes gaps in the LiDAR-based tracking: instead, the combination of LiDAR and UWB can be used in order to reduce outages in the LiDAR-based solution, whereas the latter, when available, usually improves the UWB-based results.

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Simultaneous Detection and Tracking of Pedestrian From Panoramic Laser Scanning Data

Cited by 15 publications

References 27 publications

Roadside LiDAR Vehicle Detection and Tracking Using Range and Intensity Background Subtraction

Roadside LiDAR Vehicle Detection and Tracking Using Range and Intensity Background Subtraction

Learning the Incremental Warp for 3D Vehicle Tracking in LiDAR Point Clouds

A Comparison Between Uwb and Laser-Based Pedestrian Tracking

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