一种融合的长时单目标跟踪策略

A Fused Long-Term Single Object Tracking Strategy

本文提出一种融合的长时单目标跟踪策略,融合基于深度学习的目标检测算法和基于相关滤波的单目标跟踪算法,形成两种跟踪模式,根据多种判断策略控制跟踪模式切换,能够在保证跟踪鲁棒性的情况下大幅度提高跟踪实时性。将该跟踪策略用在自制数据集ATP-UAV上,取得了较好的跟踪效果。

This paper proposes a fused long-term single object tracking strategy that combines deep learning-based object detection algorithms with correlation filter-based single object tracking algorithms.This fusion creates two tracking modes that can be switched based on multiple decision-making strategies.It significantly improves tracking real-time performance while ensuring robustness.The proposed tracking strategy is applied to the self-made ATP-UAV dataset and achieves excellent tracking results.