【作者】 向杰；

【导师】 孙伟平；

【作者基本信息】 华中科技大学 ， 计算机系统结构， 2011， 硕士

【摘要】 作为计算机视觉的核心内容,视觉跟踪在智能视频监控,机器人视觉,人机交互,人工智能等领域有重大应用价值,成为研究的热点。NVIDIA公司推出的CUDA平台可以启动大量的线程并行工作,提高算法运行速度,同样被广泛应用于图像处理,视频播放,信号处理,人工智能等领域。相比其他视觉跟踪算法,粒子滤波算法对复杂场景具备更好的鲁棒性,但很难满足实时性要求。因此,基于CUDA平台实现粒子滤波并行算法以提高算法执行效率,同时研究运动目标表观模型以提高跟踪的准确性,是十分必要的。在分析粒子滤波理论的基础上,实现了基于颜色模型的粒子滤波算法,分析了不同的预测策略和颜色空间对算法准确性的影响。实验证明基于颜色信息的粒子滤波算法跟踪准确,但计算复杂度高,且具有一定局限性。针对粒子滤波算法计算复杂度高的问题,充分利用CUDA并行运算性能,分析了粒子滤波算法的并行性,实现了基于CUDA的粒子滤波并行算法,有效加快算法运行速度,实验证明该算法可以有效加速2.5倍。针对基于颜色信息粒子滤波跟踪算法局限性,引入一种特征描述符—协方差矩阵,设计并实现了基于协方差矩阵的粒子滤波并行算法,实验表明该算法比基于颜色信息的粒子滤波算法更具有鲁棒性。更多还原

【Abstract】 As the core of computer vision in recent years, visual tracking has a great research value in intelligent video surveillance, robot vision and some other fields.Based on NVIDIA’s CUDA platform, it can start a lot of threads working in parallel. And CUDA is widely used in image processing, video play, signal processing, artificial intelligence and some other fields.Compared with other algorithms, particle filter algorithm is more robust in some complex scenes, but it is difficult to meet the requirements of real-time tracking. So it is necessary to study the parallel particle filter algorithm and make related improvements in full use of the parallel computing advantages of GPU.Firstly, it analysis the theory of particle filter algorithm, and establishes a color model embedded particle filter algorithm, tests and compares the tracking results in different prediction methods and different color space. The result shows that the color model is robust but with a high computing complexity. Also it has some limits. Then, based on the fact that the algorithm can not satisfy the real-time requirements, it presents a GPU-based parallel particle filter algorithm to make full use of GPU parallel computing advantages. The experiments shows that it can speed up 2.5 times. Finally, as to the algorithm’s limit, it introduces a more robust feature descriptor than the color model -- covariance matrix .Using covariance matrix as a feature descriptor, it implements the particle filter algorithm on GPU. Experimental results show that the algorithm is more robust.更多还原