  Complex motion patterns of natural systems, such as fish schools, bird
flocks, and cell groups, have attracted great attention from scientists for
years. Trajectory measurement of individuals is vital for quantitative and
high-throughput study of their collective behaviors. However, such data are
rare mainly due to the challenges of detection and tracking of large numbers of
objects with similar visual features and frequent occlusions. We present an
automatic and effective framework to measure trajectories of large numbers of
crowded oval-shaped objects, such as fish and cells. We first use a novel dual
ellipse locator to detect the coarse position of each individual and then
propose a variance minimization active contour method to obtain the optimal
segmentation results. For tracking, cost matrix of assignment between
consecutive frames is trainable via a random forest classifier with many
spatial, texture, and shape features. The optimal trajectories are found for
the whole image sequence by solving two linear assignment problems. We evaluate
the proposed method on many challenging data sets.
