共享存储并行多目标跟踪

高度的运算复杂性制约了粒子滤波在实际的多目标视频跟踪系统中的应用。为克服性能瓶颈，探索了一种基于OpenMP共享存储并行编程模型的粗粒度并行多目标跟踪系统的实现方法。在共享变量中维护被跟踪目标的列表，每一个目标用一个独立的粒子滤波器进行跟踪。根据处理单元的数目确定线程数量和每个线程跟踪的目标数量。与对应的串行版本相比，该并行系统将可实时跟踪的目标数目由2个增加到了8个，具有更大的实用价值。

Shared-memory parallel multi-target tracking

The application of particle filtering in real video-based multi-target tracking systems is limited because of its high computational complexity. To overcome the efficiency bottleneck, a coarse-grained parallel multi-target tracking implementation based on the OpenMP-specified shared-memory parallel programming model was explored. A list of tracked targets was maintained in a shared variable, and each target was tracked by an independent particle filter. The number of threads and the number of targets tracked by each thread were determined by the number of processing units. Compared to its corresponding optimized sequential version, the parallel implementation, which increases the number of targets in real-time tracking from 2 to 8, is of much more practical value.