Summary of Leveraging 3d Lidar Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection, by Nawfal Guefrachi et al.
Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection
by Nawfal Guefrachi, Jian Shi, Hakim Ghazzai, Ahmad Alsharoa
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework integrates LiDAR and IoT technologies for enhanced 3D object detection and activity classification in urban traffic scenarios, leveraging elevated LiDAR for precise pedestrian monitoring. The framework employs a modified Point Voxel-Region-based Convolutional Neural Network (PV-RCNN) for robust 3D detection and PointNet for classifying pedestrian activities. This approach enables insights into pedestrian behavior, promoting safer urban environments and benefiting urban traffic management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special technologies like LiDAR and IoT to make cities safer for people walking around. They create a new way to detect objects and track what people are doing in 3D space. The method helps traffic managers know where pedestrians are going and how they’re moving, which makes the city safer. |
Keywords
» Artificial intelligence » Classification » Neural network » Object detection » Rcnn
