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| 联合Mean-shift与粒子滤波器的实时视频目标跟踪算法及实现 |
| A Real-time Video Target Tracking Algorithm and Its Implementation Combining Mean-shift and Particle Filter |
| 投稿时间：2016-12-25 |
| DOI：10.16018/j.cnki.cn32-1650/n.201701005 |
| 中文关键词: 视频跟踪 粒子滤波器 Mean-shift Markov Chain Monte Carlo 重采样 |
| 英文关键词: video tracking particle filter mean-shift Markov Chain Monte Carlo resampling |
| 基金项目:辽宁省自然科学基金(2013020228) |
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| 中文摘要: |
| 粒子滤波器由于摆脱了高斯分布的约束条件，已经成为一种主流的、面向目标的非线性运动跟踪算法，广泛应用于视频压缩与检索、智能视频监控、智能人机交互等领域，其缺点是计算复杂度高、计算量庞大，无法满足实时应用的需求。针对粒子滤波器在计算量、实时性及粒子退化方面存在的问题，提出了将Mean-shift算法嵌入粒子滤波器，对重要性采样分布进行优化，以较少的采样粒子实现视频目标跟踪。仿真实验结果显示，联合Mean-shift的粒子滤波算法在目标跟踪过程中具有较好的实时性与鲁棒性。 |
| 英文摘要: |
| Due to get rid of the constraint condition of Gauss distribution, particle filter has become a mainstream, target-oriented nonlinear motion tracking algorithm, widely used in video compression and retrieval, intelligent video surveillance, intelligent human-computer interaction and other areas, the drawback is the high computational complexity and huge amount of computation, can not meet the needs of real-time applications. In this paper, the problem of particle filter in computational complexity, real-time and particle degradation is proposed. The Mean-shift algorithm is embedded in the particle filter, and the importance sampling distribution is optimized. Video target tracking is achieved with less sampling particles. The simulation results show that the joint Mean-shift particle filter algorithm has good real-time and robustness in the process of target tracking. |
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