Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking
By R. García, Olivier Aycard. IEEE transactions on intelligent transportation systems (Print), 2016
332 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Artificial Intelligence, Automotive Engineering
View Paper: https://www.semanticscholar.org/paper/5aace181cc3a4c709b9e211102fa6004c09b2568
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
- Hao Zhu
- Henry Leung
- Orhan Gazi Yalçin
- Chuanyang Sun
- Azim Eskandarian
- Ka-Veng Yuen
- Lyudmila S. Mihaylova
- Chaoxian Wu
In Artificial Intelligence
- Hao Zhu
- Henry Leung
- Orhan Gazi Yalçin
- Chuanyang Sun
- Azim Eskandarian
- Ka-Veng Yuen
- Lyudmila S. Mihaylova
- Chaoxian Wu
In Automotive Engineering
- Keqiang Li
- Hao Zhu
- Henry Leung
- Orhan Gazi Yalçin
- Chuanyang Sun
- Azim Eskandarian
- Ka-Veng Yuen
- Lyudmila S. Mihaylova
Citations in the corpus
- Overview of Environment Perception for Intelligent Vehicles (2017, background): “Therefore, many heterogeneous sensor data can be fused for vehicle tracking [145].”
- Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment (2018, background): “the environment to classify four objects of interest: pedestrian, bike, car, and truck [16].”
- Generative Adversarial Network (2019, methodology): “Methods which use multiple sensor are [15], [16], [17].”
- Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, methodology): “And [299] fuse the data from radar, lidar, and camera by the theory-of-evidence-based method for object classification, which improves the accuracy of object detection and tracking.”