Detection, classification and tracking of moving objects in a 3D environment
By Asma Azim, Olivier Aycard. 2012
195 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Luca Carlone
- Yun Chang
- Antoni Rosinol
- Nathan Hughes
- Jingnan Shi
- Marcus Abate
- Arjun Gupta
- Andrew Violette
In Artificial Intelligence
- Kazuya Takeda
- Jacob Lambert
- Alexander Carballo
- Ekim Yurtsever
- Luca Carlone
- Arjun Gupta
- Antoni Rosinol
- Junichi Yamagishi
In Electrical and Electronic Engineering
Citations in the corpus
- A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “Object tracking is also often referred to as multiple object tracking (MOT) [180] and detection and tracking of multiple objects (DATMO) [181].”
- Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, methodology): “…of the existing literature in robotics models the dynamic targets as a single 3D point (Chojnacki and Indelman 2018), or with a 3D pose and rely on lidar (Azim and Aycard 2012), RGB-D cameras (Aldoma et al. 2013), monocular cameras (Li et al. 2018b), and visual-inertial sensing (Qiu et al. 2019).”
- Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors (2019, methodology): “In terms of object identification, a major approach is feature-based machine learning classification (Wojke and Häselich, 2012; Azim and Aycard, 2012; Lee and Coifman, 2012; Cheng et al., 2014; Zangenehpour et al., 2015).”