Parameterizable FPGA Framework for Particle Filter Based Object Tracking in Video
Parameterizable FPGA Framework for Particle Filter Based Object Tracking in Video
复制标题
用于视频中基于粒子滤波器的对象跟踪的参数化 FPGA 框架
DOI：
10.1109/vlsid.2015.11
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Patkar
中科院分区：
文献类型：
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作者：
Pinalkumar Engineer;R. Velmurugan;S. Patkar
在嵌入式平台（FPGA）上实现基于粒子滤波的实时视频目标跟踪是一个具有挑战性的问题，因为其资源占用和计算复杂度。此外，对算法的微小改变将需要改变硬件。为了解决这些问题，我们提出了一个参数化的FPGA框架的粒子滤波器为基础的目标跟踪算法。这种可参数化的实现可以用于各种图像序列、对象大小和粒子数量。通过改变几个参数，这种参数化导致硬件资源的适当变化，从而导致算法的有效实时操作。实验结果表明，从实现更好的跟踪和所提出的架构可以运行粒子滤波算法的平均650帧每秒的彩色视频序列。
Real-time particle filter based object tracking in videos on embedded platforms (FPGA) is challenging because of its resource usage and computational complexity. Furthermore, minor changes to the algorithm will need changes in the hardware. To address these issues, we propose a parametrizable FPGA framework for particle filter based object tracking algorithm. This parametrizable implementation can be used for various image sequences, object sizes and number of particles. By changing few parameters, this parametrization leads to appropriate changes in hardware resources resulting in efficient real-time operation of the algorithm. Experimental results show better tracking from the implementation and the proposed architecture can run particle filter algorithm for a color video sequence with 650 fps on average.