Video analysis is currently the main non-intrusive method for the study of collective behaviour . However, 3D-to-2D projection leads to overlapping of observed objects. The situation is further complicated by the absence of stall shapes for the majority of living objects. Fortunately, living objects often possess a certain symmetry which was used as a basis for morphological fingerprinting. This technique allowed us to record forms of symmetrical objects in a pose-invariant way. When combined with image skeletonization, this gives a robust, nonlinear, optimization-free, and fast method for detection of overlapping objects, even without any rigid pattern. This novel method was verified on fish (European bass, Dicentrarchus labrax and Tiger Barbs , Puntius tetrazona) swimming in a reasonably small tank, which forced them to exhibit a large variety of shapes. The correct number of objects was determined for 88\% of overlaps and the mean Dice-S \o%DIFDELCMD < }%%% rensen coefficient was 0:84, implying that this method is feasible in real-life applications such as toxicity testing.\\%DIF > of overlaps, and the mean Dice-Sorensen coefficient was around 0.83. This implies that this method is feasible in real-life applications such as toxicity testing.
Video analysis is currently the main non-intrusive method for the study of collective behavior . However, 3D-to-2D projection leads to overlapping of observed objects. The situation is further complicated by the absence of stall shapes for the majority of living objects. Fortunately, living objects often possess a certain symmetry which was used as a basis for morphological fingerprinting. This technique allowed us to record forms of symmetrical objects in a pose-invariant way. When combined with image skeletonization, this gives a robust, nonlinear, optimization-free, and fast method for detection of overlapping objects, even without any rigid pattern. This novel method was verified on fish (European bass, Dicentrarchus labrax , and tiger barbs , Puntius tetrazona) swimming in a reasonably small tank, which forced them to exhibit a large variety of shapes. Compared with manual detection, the correct number of objects was determined for %DIFDELCMD < }%%% up to almost 90\\%DIF > of overlaps, and the mean Dice-Sorensen coefficient was around 0.83. This implies that this method is feasible in real-life applications such as toxicity testing.
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