  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 up to almost $90 \%$ 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.
