Moving Target Tracking Algorithm Based On Openmp Parallelism

The development of multi-core parallel programming technology has brought the new dawn for those programs which are limited to single-core, especially for those real-time programs that have dead-line constraints. In intelligence transportation system, serial video-based tracking programs will encounter the serious performance problems if excessive targets appear in a scene, consequently the real-time cannot be guaranteed. How to design and develop the target tracking programs based on multi-core technology to take full advantages of the performance promotion, becomes an interesting issue to improve the real-time of the object tracking program.Mean shift algorithm and particle filter algorithm are two kind of commonly used target tracking algorithms. Mean shift is a non-parametric method for climbing density gradients, which iteratively finds the new position of the target. Particle filter algorithm has outstanding tracking performance under the complex environment. It is a recursive Bayes filter based on sample set, and has strong anti-interference capability. The program still continues to track the target in a short time after missing target by occluded. But the large amounts of calculation in real-time program easily become the system bottleneck. In this paper, we try to parallel the two algorithms by analyzing those characteristics to solve the problems such as intensive computation and slow speed. The bottlenecks and hotspots of program are detected with Intel Parallel Studio tools, and then the parallelization is implemented to these areas based on the OpenMP.In addition, this paper proposes a parallel pipeline model for vehicle tracking, and makes the video frames processed in a pipeline. Each stage of the parallel pipeline model is mapped to processors, and runs concurrently in the form of parallel threads to accelerate the program. The advantages and disadvantages of the model, the speedup of actual program are also discussed, respectively.In the quad-core environment, we compare the runtime statistics of serial/parallel mean shift algorithm and particle filter algorithm, and respectively calculate the parallel speedup of the programs. The experimental results show the superiority of multi-core parallel programming technology to enhance application performance. It takes full advantage of the computing resources of multi-core processors, thus overcomes the performance bottlenecks of serial tracking algorithms, and has achieved the optimization goal as we expected.