Understanding fish behavior during typhoon events in real-life underwater environments

Abstract

The study of fish populations in their own natural environment is a task that has usually been tackled in invasive ways which inevitably influenced the behavior of the fish under observation. Recent projects involving the installation of permanent underwater cameras (e.g. the Fish4Knowledge (F4K) project, for the observation of Taiwan's coral reefs) allow to gather huge quantities of video data, without interfering with the observed environment, but at the same time require the development of automatic processing tools, since manual analysis would be impractical for such amounts of videos. Event detection is one of the most interesting This research was funded by European Commission FP7 grant 257024, for the Fish4Knowledge project (www.fish4knowledge.eu). Appl (2014) 70:199-236 aspects from the biologists' point of view, since it allows the analysis of fish activity during particular events, such as typhoons. In order to achieve this goal, in this paper we present an automatic video analysis approach for fish behavior understanding during typhoon events. The first step of the proposed system, therefore, involves the detection of "typhoon" events and it is based on video texture analysis and on classification by means of Support Vector Machines (SVM). As part of our behavior understanding efforts, trajectory extraction and clustering have been performed to study the differences in behavior when disruptive events happen. The integration of event detection with fish behavior understanding surpasses the idea of simply detecting events by low-level features analysis, as it supports the full semantic comprehension of interesting events.

A rule-based event detection system for real-life underwater domain

“…These results show that the system performance is comparable to those of by state-of-the-art approaches performing on much simpler events [1,9].…”

“…To tackle these problems, we developed an algorithm specifically designed for tracking fish in unconstrained underwater environments [6,9]. It is based on a covariance representation of fish features [52].…”

“…To our knowledge, our proposed system is one of the first approaches addressing behavioural event detection for the underwater domain. A previous attempt was proposed by Spampinato et al [9] but it aimed at detecting environmental events (e.g. typhoons and storms), and at investigating how fish behaviour changes when such events occur.…”

LifeCLEF 2016: Multimedia Life Species Identification Challenges

“…[49,19], the most important step in the automated visual analysis has been done in the EU-funded Fish4Knowledge (F4K) 17 project, where computer vision methods were developed to extract information about fish density and richness from videos taken by underwater cameras installed at coral reefs in Taiwan [62,63,6,61]. Since the F4K project, many researchers have directed their attention towards underwater video analysis [53,55], including some recent initiatives by the National Oceanographic and Atmospheric Administration (NOAA) [57] and the fish identification task at LifeCLEF 2014 and 2015 [12,13,60].…”

An automated chimpanzee identification system using face detection and recognition

“…They trained the system on lion faces and showed that the results can be used to classify basic locomotive actions. Spampinato et al [19,20] proposed a system for fish detection, tracking, and species classification in natural underwater environment. They first detect fishes using a combination of a Gaussian mixture model and moving average algorithms.…”