Automatic trajectory measurement of large numbers of crowded objects

TL;DR

This paper introduces an automatic framework for measuring trajectories of large numbers of crowded oval-shaped objects, addressing detection and tracking challenges in complex motion patterns like fish schools and cell groups.

Contribution

It proposes a novel dual ellipse locator and a variance minimization active contour method for improved detection and segmentation, along with a trainable cost matrix for tracking.

Findings

Effective in crowded scenarios with occlusions

Accurate trajectory measurement demonstrated on challenging datasets

Outperforms existing methods in detection and tracking quality

Abstract

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.