Automatic analysis of deep-water remotely operated vehicle footage for estimation of Norway lobster abundance
Ching Soon TAN1, Phooi Yee LAU1(), Paulo L. CORREIA2, Aida CAMPOS3,4
1. Centre for Computing and Intelligent Systems, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia 2. Instituto de Telecomunicações, Instituto Superior Técnico, Av. Rovisco Pais, 1, Lisbon 1049-001, Portugal 3. Instituto Português do Mar e da Atmosfera (IPMA), Divisão de Modelação e Gestão de Recursos da Pesca, Lisbon 1749-077, Portugal 4. Centro de Ciências do Mar (CCMAR) - Campus de Gambelas, Faro 8005-139, Portugal
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.
