A video multitracking system for quantification of individual behavior in a large fish shoal: Advantages and limits

Abstract

The capability of a new multitracking system to track a large number of unmarked fish (up to 100) is evaluated. This system extrapolates a trajectory from each individual and analyzes recorded sequences that are several minutes long. This system is very efficient in statistical individual tracking, where the individual's identity is important for a short period of time in comparison with the duration of the track. Individual identification is typically greater than 99%. Identification is largely efficient (more than 99%) when the fish images do not cross the image of a neighbor fish. When the images of two fish merge (occlusion), we consider that the spot on the screen has a double identity. Consequently, there are no identification errors during occlusions, even though the measurement of the positions of each individual is imprecise. When the images of these two merged fish separate (separation), individual identification errors are more frequent, but their effect is very low in statistical individual tracking. On the other hand, in complete individual tracking, where individual fish identity is important for the entire trajectory, each identification error invalidates the results. In such cases, the experimenter must observe whether the program assigns the correct identification, and, when an error is made, must edit the results. This work is not too costly in time because it is limited to the separation events, accounting for fewer than 0.1% of individual identifications. Consequently, in both statistical and rigorous individual tracking, this system allows the experimenter to gain time by measuring the individual position automatically. It can also analyze the structural and dynamic properties of an animal group with a very large sample, with precision and sampling that are impossible to obtain with manual measures.

The use of visual and automatized behavioral markers to assess methodologies: a study case on PIT-tagging in the Alpine newt

“…The use of a newly developed video-tracking software (Ethovision® XT 7) allowed us to quantify variables such as distance, movement, and velocity. This computerized method has an advantage over standard visual methods in that it determines in a very accurate way the position of an individual and allows processing of a large amount of data in a relatively short time period (Delcourt et al, 2006;Delcourt et al, 2009;Eddins et al, 2010). Both methods proved to be complementary with respect to each other in analyzing different behavioral aspects: Visual observations assess complex behaviors such as feeding and breeding, while video tracking assesses more quantitative patterns of locomotor activity.…”

Redefining membership in animal groups

“…A custom application, designed in-house, extracted the positions of all the fish from each frame of video and calculated the trajectory of each fish. A similar application was recently described in some detail by Delcourt, Becco, Vandewalle, and Poncin (2009). The fish were allowed to habituate to the tank for the first 5 min of each session, and minutes 5-10 of each 30-min session were analyzed (only 5 min of data were analyzed per session because of limitations of the tracking software).…”

Posture tracking of multiple individual fish for behavioral monitoring with visual sensors

“…The interaction between two fish was also discussed through experiments. A multitracking system was developed to record the movement trajectories of multiple fish (Delcourt et al, 2006;Delcourt et al, 2009). The multitracking system tracked the fish movements by predicting the theoretical position of each individual and matched to the nearest neighbor of the target fish (Delcourt et al, 2009).…”

“…A multitracking system was developed to record the movement trajectories of multiple fish (Delcourt et al, 2006;Delcourt et al, 2009). The multitracking system tracked the fish movements by predicting the theoretical position of each individual and matched to the nearest neighbor of the target fish (Delcourt et al, 2009). A Kalman filter and particle filter are the most widely used techniques for predicting the positions of moving targets for tracking tasks, including the movement tracking of multiple fish (Fontaine et al, 2008;Pinkiewicz et al, 2008;).…”