Online learning for 3D LiDAR-based human detection: experimental analysis of point cloud clustering and classification methods

Yan, Zhi; Duckett, Tom; Bellotto, Nicola

doi:10.1007/s10514-019-09883-y

Cited by 85 publications

(63 citation statements)

References 49 publications

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“…There are several algorithms including machine/deep learning methods available for processing Lidar point clouds and spatial data sets to achieve a reliable result for change detection of moving objects [25,28,29]. For example, Shirowzhan, Sepasgozar [25] applied machine learning and point-based algorithms to show 3D changes of urban environment over time using bi-temporal point clouds.…”

Section: Machine/deep Learning Algorithmsmentioning

confidence: 99%

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

Shirowzhan

Tan

Sepasgozar

2020

IJGI

124

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Smart technologies are advancing, and smart cities can be made smarter by increasing the connectivity and interactions of humans, the environment, and smart devices. This paper discusses selective technologies that can potentially contribute to developing an intelligent environment and smarter cities. While the connectivity and efficiency of smart cities is important, the analysis of the impact of construction development and large projects in the city is crucial to decision and policy makers, before the project is approved. This raises the question of assessing the impact of a new infrastructure project on the community prior to its commencement—what type of technologies can potentially be used for creating a virtual representation of the city? How can a smart city be improved by utilizing these technologies? There are a wide range of technologies and applications available but understanding their function, interoperability, and compatibility with the community requires more discussion around system designs and architecture. These questions can be the basis of developing an agenda for further investigations. In particular, the need for advanced tools such as mobile scanners, Geospatial Artificial Intelligence, Unmanned Aerial Vehicles, Geospatial Augmented Reality apps, Light Detection, and Ranging in smart cities is discussed. In line with smart city technology development, this Special Issue includes eight accepted articles covering trending topics, which are briefly reviewed.

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Section: Machine/deep Learning Algorithmsmentioning

confidence: 99%

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

Shirowzhan

Tan

Sepasgozar

2020

IJGI

124

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“…Again, the ROS implementation of the depth-based detector in [151] is available 4 . In addition, an offline version of the 3D lidar-based approach in [234] is implemented as a ROS module 5 . The authors of the RGBD-based detector in [156] provide the implementation of their algorithm 6 .…”

Section: Discussionmentioning

confidence: 99%

“…Depth information enables more robust detection algorithms. For example, the authors in [234] proposed an online learning method based on a 3D lidar cluster detector, a multi-target tracker, a human classifier and a sample generator. The cluster detection starts by removing the ground plane, then point clusters are extracted from the point clouds using the Euclidean distance in 3D space and finally a human-like volumetric model is fitted to the clusters for filtering.…”

Section: Other Detection Models A) Spatio-temporal Featuresmentioning

confidence: 99%

“…Some other works on 3D visual tracking include [59], [166], [194], which used a single camera with a multi-Bernoulli mixture tracking filter. Some works with 3D lidar sensors include [114], [207], [234], which proposed online classification of humans for 3D lidar tracking. In [175], both camera and lidar data are used to improve people tracking.…”

Section: Multiple Pedestrian Trackingmentioning

confidence: 99%

See 1 more Smart Citation

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Camara

Bellotto

Coşar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behaviour modelling, prediction and interaction control.

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“…More closely related to our work, several researchers have used 3D LiDARs for the detection of pedestrians around the autonomous vehicles [ 20 , 21 , 22 , 23 ]. For autonomous driving, this is an important sensing capability as it is needed to avoid collisions with pedestrians.…”

Section: Related Workmentioning

confidence: 99%

Using a Rotating 3D LiDAR on a Mobile Robot for Estimation of Person’s Body Angle and Gender

Brščić

Evans

Rehm

et al. 2020

Sensors

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We studied the use of a rotating multi-layer 3D Light Detection And Ranging (LiDAR) sensor (specifically the Velodyne HDL-32E) mounted on a social robot for the estimation of features of people around the robot. While LiDARs are often used for robot self-localization and people tracking, we were interested in the possibility of using them to estimate the people’s features (states or attributes), which are important in human–robot interaction. In particular, we tested the estimation of the person’s body orientation and their gender. As collecting data in the real world and labeling them is laborious and time consuming, we also looked into other ways for obtaining data for training the estimators: using simulations, or using LiDAR data collected in the lab. We trained convolutional neural network-based estimators and tested their performance on actual LiDAR measurements of people in a public space. The results show that with a rotating 3D LiDAR a usable estimate of the body angle can indeed be achieved (mean absolute error 33.5 ° ), and that using simulated data for training the estimators is effective. For estimating gender, the results are satisfactory (accuracy above 80%) when the person is close enough; however, simulated data do not work well and training needs to be done on actual people measurements.

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Online learning for 3D LiDAR-based human detection: experimental analysis of point cloud clustering and classification methods

Cited by 85 publications

References 49 publications

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Using a Rotating 3D LiDAR on a Mobile Robot for Estimation of Person’s Body Angle and Gender

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