Dim and Small Target Tracking Using an Improved Particle Filter Based on Adaptive Feature Fusion

Abstract

Particle filters have been widely used in dim and small target tracking, which plays a significant role in navigation applications. However, their characteristics, such as difficulty of expressing features for dim and small targets and lack of particle diversity caused by resampling, lead to a considerable negative impact on tracking performance. In the present paper, we propose an improved resampling particle filter algorithm based on adaptive multi-feature fusion to address the drawbacks of particle filters for dim and small target tracking and improve the tracking performance. We first establish an observation model based on the adaptive fusion of the features of the weighted grayscale intensity, edge information, and wavelet transform. We then generate new particles based on residual resampling by combining the target position in the previous frame and the particles in the current frame with higher weights, with the tracking accuracy and particle diversity improving simultaneously. The experimental results demonstrate that our proposed method achieves a high tracking performance with a distance accuracy of 77.2% and a running speed of 106 fps, respectively, meaning that it will have a promising prospect in dim and small target tracking applications.

IRSDT: A Framework for Infrared Small Target Tracking with Enhanced Detection

“…Huo et al [39] proposed an improved resampling particle filter algorithm based on adaptive multi-feature fusion. They first establish an observation model based on the adaptive fusion of the features of the weighted grayscale intensity, edge information, and wavelet transform and then generate new particles based on residual resampling by combining the target position in the previous frame and the particles in the current frame with higher weights to improve the tracking accuracy and stability.…”

STRCFD: Small Maneuvering Object Tracking via Improved STRCF and Redetection in Near Infrared Videos

“…Yet, the morphological method depends on the template, and the realtime performance of the SR methods is inefficient. Moreover, Huo et al [28] propose a multi-feature fusion model, which is embedded in the particle filter for small target tracking. Once the target moves outside the search region, the detection mechanism [29]- [31] can be effective in tracking migration.…”