Computer Vision for Avoidance is a computational framework that enables machines to detect, identify, and respond to environmental obstacles or hazards through visual data interpretation, distinguishing itself from general computer vision by its specialized focus on obstacle detection and reactive navigation for avoidance purposes.
87
Publications
2.7K
Citations
286
Authors
100
Institutions
Publications per year
2017–2026
25
Parents
Publications
87
Visual sonar: fast obstacle avoidance using monocular vision
S. Lenseir, Manuela Veloso · 2004 · 132 citations
Vision-based detection and tracking of aerial targets for UAV collision avoidance
Luis Mejías, Shamus McNamara, John Lai et al. · 2010 · 79 citations · Full text
1–5 of 87
Authors
286
- GD
3
h-index
- RW
3
h-index
- CD
3
h-index
- BD
3
h-index
- CR
2
h-index
1–5 of 286
Institutions
100
Delft University of Technology
Delft, The Netherlands
4 h-index
14 publications
470 citations
National Yang Ming Chiao Tung University
Hsinchu, Taiwan
3 h-index
7 publications
261 citations
Hungarian Academy of Sciences
Budapest, Hungary
3 h-index
6 publications
119 citations
Brigham Young University
Provo, United States
3 h-index
10 publications
212 citations
Queensland University of Technology
Brisbane, Australia
2 h-index
5 publications
332 citations
Nanyang Technological University
Singapore, Singapore
2 h-index
5 publications
140 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
3 publications
500 citations
HUN-REN Institute for Computer Science and Control
Budapest, Hungary
2 h-index
5 publications
95 citations
1–8 of 100
Venues
- Journal of Intelligent & Robotic Systems
2 publications · 40 citations
2
h-index
- AIAA Guidance, Navigation, and Control Conference
Conference · 2 publications · 33 citations
2
h-index
- IEEE Access
2 publications · 51 citations
2
h-index
- Machine Vision and Applications
1 publications · 20 citations
1
h-index
- IEEJ Transactions on Electronics Information and Systems
1 publications · 21 citations
1
h-index
1–5