摘要: 粒子滤波作为一种基于贝叶斯估计的算法,在处理非线性运动目标跟踪问题上具有特殊的优势。基于此,提出一种基于粒子滤波和Mean-shift的混合跟踪算法(KMSEPF)。KMSEPF算法对一般的Mean-shift和粒子滤波混合算法进行改进。结果证明,KMSEPF算法与混合算法MSEPF相比,在计算效率提高的同时,跟踪准确性和处理遮挡的能力没有下降。
关键词:
粒子滤波,
Mean-shift算法,
目标跟踪
Abstract: As an algorithm based on Bayesian estimation, particle filtering is predominant on tracking nonlinear moving target. This paper proposes an algorithm, which is based on Mean-shift and particle filtering, named K-means and Mean-shift Embedded Particle Filter(KMSEPF). The KMSEPF algorithm improves the general mixture algorithms which are based on particle filtering and Mean-shift. Results show that the algorithm reduces the computation complexity, while maintains the high precision and the ability to control the occlusion, compared with the MSEPF algorithm.
Key words:
particle filtering,
Mean-shift algorithm,
object tracking
中图分类号:
蒋 旻;许 勤;尚 涛;高伟义. 基于粒子滤波和Mean-shift的跟踪算法[J]. 计算机工程, 2010, 36(5): 21-22,2.
JIANG Min; XU Qin; SHANG Tao; GAO Wei-yi. Tracking Algorithm Based on Particle Filtering and Mean-shift[J]. Computer Engineering, 2010, 36(5): 21-22,2.