Three-dimensional Tracking of a Large Number of High Dynamic Objects from Multiple Views using Current Statistical Model

TL;DR

This paper introduces a novel Bayesian multi-view 3D tracking method using a current statistical model-based Kalman particle filter, significantly improving tracking accuracy for dynamic, similar, and clustered objects in biological studies.

Contribution

The paper proposes the CSKPF algorithm, combining current statistical models with Kalman particle filtering to enhance multi-object 3D tracking accuracy and robustness.

Findings

Improved tracking integrity, continuity, and precision over existing methods.

Effective suppression of measurement noise in complex multi-object scenarios.

Validated through simulations and real fruitfly cluster experiments.

Abstract

Three-dimensional tracking of multiple objects from multiple views has a wide range of applications, especially in the study of bio-cluster behavior which requires precise trajectories of research objects. However, there are significant temporal-spatial association uncertainties when the objects are similar to each other, frequently maneuver, and cluster in large numbers. Aiming at such a multi-view multi-object 3D tracking scenario, a current statistical model based Kalman particle filter (CSKPF) method is proposed following the Bayesian tracking-while-reconstruction framework. The CSKPF algorithm predicts the objects' states and estimates the objects' state covariance by the current statistical model to importance particle sampling efficiency, and suppresses the measurement noise by the Kalman filter. The simulation experiments prove that the CSKPF method can improve the tracking integrity, continuity, and precision compared with the existing constant velocity based particle filter (CVPF) method. The real experiment on fruitfly clusters also confirms the effectiveness of the CSKPF method.