Pedestrian Detection with LiDAR Technology in Smart-City Deployments–Challenges and Recommendations
This paper describes a real case implementation of an automatic pedestrian-detection solution, implemented in the city of Aveiro, Portugal, using affordable LiDAR technology and open, publicly available, pedestrian-detection frameworks based on machine-learning algorithms. The presented solution mak...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/12/3/65 |
| _version_ | 1797612564290469888 |
|---|---|
| author | Pedro Torres Hugo Marques Paulo Marques |
| author_facet | Pedro Torres Hugo Marques Paulo Marques |
| author_sort | Pedro Torres |
| collection | DOAJ |
| description | This paper describes a real case implementation of an automatic pedestrian-detection solution, implemented in the city of Aveiro, Portugal, using affordable LiDAR technology and open, publicly available, pedestrian-detection frameworks based on machine-learning algorithms. The presented solution makes it possible to anonymously identify pedestrians, and extract associated information such as position, walking velocity and direction in certain areas of interest such as pedestrian crossings or other points of interest in a smart-city context. All data computation (3D point-cloud processing) is performed at edge nodes, consisting of NVIDIA Jetson Nano and Xavier platforms, which ingest 3D point clouds from Velodyne VLP-16 LiDARs. High-performance real-time computation is possible at these edge nodes through CUDA-enabled GPU-accelerated computations. The MQTT protocol is used to interconnect publishers (edge nodes) with consumers (the smart-city platform). The results show that using currently affordable LiDAR sensors in a smart-city context, despite the advertising characteristics referring to having a range of up to 100 m, presents great challenges for the automatic detection of objects at these distances. The authors were able to efficiently detect pedestrians up to 15 m away, depending on the sensor height and tilt. Based on the implementation challenges, the authors present usage recommendations to get the most out of the used technologies. |
| first_indexed | 2024-03-11T06:42:56Z |
| format | Article |
| id | doaj.art-958f67f5614c4873baff7dc36a77ceb1 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-11T06:42:56Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-958f67f5614c4873baff7dc36a77ceb12023-11-17T10:26:36ZengMDPI AGComputers2073-431X2023-03-011236510.3390/computers12030065Pedestrian Detection with LiDAR Technology in Smart-City Deployments–Challenges and RecommendationsPedro Torres0Hugo Marques1Paulo Marques2Instituto Politécnico de Castelo Branco, Av. Pedro Álvares Cabral, n°12, 6000-084 Castelo Branco, PortugalInstituto Politécnico de Castelo Branco, Av. Pedro Álvares Cabral, n°12, 6000-084 Castelo Branco, PortugalInstituto Politécnico de Castelo Branco, Av. Pedro Álvares Cabral, n°12, 6000-084 Castelo Branco, PortugalThis paper describes a real case implementation of an automatic pedestrian-detection solution, implemented in the city of Aveiro, Portugal, using affordable LiDAR technology and open, publicly available, pedestrian-detection frameworks based on machine-learning algorithms. The presented solution makes it possible to anonymously identify pedestrians, and extract associated information such as position, walking velocity and direction in certain areas of interest such as pedestrian crossings or other points of interest in a smart-city context. All data computation (3D point-cloud processing) is performed at edge nodes, consisting of NVIDIA Jetson Nano and Xavier platforms, which ingest 3D point clouds from Velodyne VLP-16 LiDARs. High-performance real-time computation is possible at these edge nodes through CUDA-enabled GPU-accelerated computations. The MQTT protocol is used to interconnect publishers (edge nodes) with consumers (the smart-city platform). The results show that using currently affordable LiDAR sensors in a smart-city context, despite the advertising characteristics referring to having a range of up to 100 m, presents great challenges for the automatic detection of objects at these distances. The authors were able to efficiently detect pedestrians up to 15 m away, depending on the sensor height and tilt. Based on the implementation challenges, the authors present usage recommendations to get the most out of the used technologies.https://www.mdpi.com/2073-431X/12/3/65pedestrian detectionLiDAR3D point cloudsROSsmart citiestraffic mobility |
| spellingShingle | Pedro Torres Hugo Marques Paulo Marques Pedestrian Detection with LiDAR Technology in Smart-City Deployments–Challenges and Recommendations Computers pedestrian detection LiDAR 3D point clouds ROS smart cities traffic mobility |
| title | Pedestrian Detection with LiDAR Technology in Smart-City Deployments–Challenges and Recommendations |
| title_full | Pedestrian Detection with LiDAR Technology in Smart-City Deployments–Challenges and Recommendations |
| title_fullStr | Pedestrian Detection with LiDAR Technology in Smart-City Deployments–Challenges and Recommendations |
| title_full_unstemmed | Pedestrian Detection with LiDAR Technology in Smart-City Deployments–Challenges and Recommendations |
| title_short | Pedestrian Detection with LiDAR Technology in Smart-City Deployments–Challenges and Recommendations |
| title_sort | pedestrian detection with lidar technology in smart city deployments challenges and recommendations |
| topic | pedestrian detection LiDAR 3D point clouds ROS smart cities traffic mobility |
| url | https://www.mdpi.com/2073-431X/12/3/65 |
| work_keys_str_mv | AT pedrotorres pedestriandetectionwithlidartechnologyinsmartcitydeploymentschallengesandrecommendations AT hugomarques pedestriandetectionwithlidartechnologyinsmartcitydeploymentschallengesandrecommendations AT paulomarques pedestriandetectionwithlidartechnologyinsmartcitydeploymentschallengesandrecommendations |