Simple Two-Dimensional Object Tracking based on a Graph Algorithm

TL;DR

This paper introduces a novel graph-based algorithm for tracking fast-moving micrometer-sized objects in microscopic videos, combining blob recognition, shape features, and graph algorithms for high accuracy.

Contribution

The work presents a new integrated approach that combines multiple algorithms in a novel way for deterministic object tracking in microscopic videos.

Findings

High accuracy in object and trajectory recognition

Superior recognition rates compared to similar algorithms

Effective tracking of fast-moving objects over large distances

Abstract

The visual observation and tracking of cells and other micrometer-sized objects has many different biomedical applications. The automation of those tasks based on computer methods helps in the evaluation of such measurements. In this work, we present a general purpose algorithm that excels at evaluating deterministic behavior of micrometer-sized objects. Our concrete application is the tracking of fast moving objects over large distances along deterministic trajectories in a microscopic video. Thereby, we are able to determine characteristic properties of the objects. For this purpose, we use a set of basic algorithms, including blob recognition, feature-based shape recognition and a graph algorithm, and combined them in a novel way. An evaluation of the algorithms performance shows a high accuracy in the recognition of objects as well as of complete trajectories. Moreover, a direct comparison to a similar algorithm shows superior recognition rates.