  Zebrafish is an excellent model organism, which has been widely used in the
fields of biological experiments, drug screening, and swarm intelligence. In
recent years, there are a large number of techniques for tracking of zebrafish
involved in the study of behaviors, which makes it attack much attention of
scientists from many fields. Multi-target tracking of zebrafish is still facing
many challenges. The high mobility and uncertainty make it difficult to predict
its motion; the similar appearances and texture features make it difficult to
establish an appearance model; it is even hard to link the trajectories because
of the frequent occlusion. In this paper, we use particle filter to approximate
the uncertainty of the motion. Firstly, by analyzing the motion characteristics
of zebrafish, we establish an efficient hybrid motion model to predict its
positions; then we establish an appearance model based on the predicted
positions to predict the postures of every targets, meanwhile weigh the
particles by comparing the difference of predicted pose and observation pose ;
finally, we get the optimal position of single zebrafish through the weighted
position, and use the joint particle filter to process trajectory linking of
multiple zebrafish.

Computer Vision and Pattern Recognition