摘要：

根据积分思想和粒子滤波理论，提出一种运动物体自适应跟踪算法。建立多特征的跟踪观测模型，采用积分策略对多个特征模型进行自适合融合和更新，通过观测似然度更新粒子滤波所需粒子数量及其分布，并在动态场景下对运动物体进行跟踪。实验结果表明，该算法相对于传统的跟踪算法在跟踪精度和实时性方面有所提高，具有较好的鲁棒性。

关键词: 跟踪, 特征模型, 自适应模型融合, 观测似然度, 粒子滤波

Abstract:

An adaptive tracking algorithm based on integral fusion thoughts and particle filter theory is proposed for video objects in motion. More features measurement models are used and the introduction of integral strategy fuses and updates them adaptively. Based on observation likelihood, the algorithm updates the number of particles and proposal density, and applies particle filtering for tracking under dynamic scenes. Experiments results demonstrate that the proposed algorithm improves the tracking precision and real-time with more robustness, contrasting to traditional tracking algorithm.

Key words: tracking, characteristic model, adaptive model fusion, observation likelihood, particle filtering

中图分类号: 

• TP391