Automated Stitching of Coral Reef Images and Extraction of Features for Damselfish Shoaling Behavior Analysis
This work addresses the lack of base behavior information for damselfish, aiding reef managers in planning stress and disaster responses, but it is incremental as it focuses on pre-processing rather than novel analysis methods.
The researchers tackled the problem of analyzing damselfish shoaling behavior by developing a pre-processing system that uses color correction and image stitching to handle distorted videos from the wild, enabling manual feature extraction for behavior analysis.
Behavior analysis of animals involves the observation of intraspecific and interspecific interactions among various organisms in the environment. Collective behavior such as herding in farm animals, flocking of birds, and shoaling and schooling of fish provide information on its benefits on collective survival, fitness, reproductive patterns, group decision-making, and effects in animal epidemiology. In marine ethology, the investigation of behavioral patterns in schooling species can provide supplemental information in the planning and management of marine resources. Currently, damselfish species, although prevalent in tropical waters, have no adequate established base behavior information. This limits reef managers in efficiently planning for stress and disaster responses in protecting the reef. Visual marine data captured in the wild are scarce and prone to multiple scene variations, primarily caused by motion and changes in the natural environment. The gathered videos of damselfish by this research exhibit several scene distortions caused by erratic camera motions during acquisition. To effectively analyze shoaling behavior given the issues posed by capturing data in the wild, we propose a pre-processing system that utilizes color correction and image stitching techniques and extracts behavior features for manual analysis.