Name
Title | ||
|---|---|---|
Automatic analysis of deep-water remotely operated vehicle footage for estimation of Norway lobster abundance. |
Abstract | ||
|---|---|---|
Underwater imaging is being used increasingly by marine biologists as a means to assess the abundance of marine resources and their biodiversity. Previously, we developed the first automatic approach for estimating the abundance of Norway lobsters and counting their burrows in video sequences captured using a monochrome camera mounted on trawling gear. In this paper, an alternative framework is proposed and tested using deep-water video sequences acquired via a remotely operated vehicle. The proposed framework consists of four modules: (1) preprocessing, (2) object detection and classification, (3) object-tracking, and (4) quantification. Encouraging results were obtained from available test videos for the automatic video-based abundance estimation in comparison with manual counts by human experts (ground truth). For the available test set, the proposed system achieved 100% precision and recall for lobster counting, and around 83% precision and recall for burrow detection. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1631/FITEE.1700720 | Frontiers of IT & EE |
Keywords | Field | DocType |
Object detection, Object tracking, Feature extraction, Remotely operated vehicle (ROV), TP391 | Computer vision,Object detection,Computer science,Control theory,Precision and recall,Feature extraction,Video tracking,Ground truth,Abundance estimation,Artificial intelligence,Remotely operated vehicle,Test set | Journal |
Volume | Issue | ISSN |
19 | 8 | 2095-9184 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ching Soon Tan | 1 | 0 | 0.34 |
| Phooi Yee Lau | 2 | 0 | 0.34 |
| Paulo Lobato Correia | 3 | 281 | 31.59 |
| Campos, A. | 4 | 0 | 1.35 |