Applying Dynamic Model for Multiple Manoeuvring Target Tracking Using Particle Filtering

TL;DR

This paper introduces the Deformation Detection Particle Filter (DDPF), a novel method that enhances multiple maneuvering target tracking accuracy by dynamically updating target models based on deformation detection, outperforming basic particle filters.

Contribution

The paper proposes DDPF, which integrates deformation detection into particle filtering for improved tracking of maneuvering targets, especially during rotations and scaling.

Findings

DDPF outperforms basic SIR-PF in tracking accuracy.

The approach effectively detects and updates target models during deformation.

Real airshow videos validate the method's effectiveness.

Abstract

In this paper, we applied a dynamic model for manoeuvring targets in SIR particle filter algorithm for improving tracking accuracy of multiple manoeuvring targets. In our proposed approach, a color distribution model is used to detect changes of target's model . Our proposed approach controls deformation of target's model. If deformation of target's model is larger than a predetermined threshold, then the model will be updated. Global Nearest Neighbor (GNN) algorithm is used as data association algorithm. We named our proposed method as Deformation Detection Particle Filter (DDPF) . DDPF approach is compared with basic SIR-PF algorithm on real airshow videos. Comparisons results show that, the basic SIR-PF algorithm is not able to track the manoeuvring targets when the rotation or scaling is occurred in target' s model. However, DDPF approach updates target's model when the rotation or scaling is occurred. Thus, the proposed approach is able to track the manoeuvring targets more efficiently and accurately.