论文标题
用极小矩阵的在线和实时对象跟踪算法
Online and Real-time Object Tracking Algorithm with Extremely Small Matrices
论文作者
论文摘要
在线和实时对象跟踪是一个有趣的工作负载，可用于实时的一系列视频序列，用于跟踪对象（例如，汽车，人类，动物）。对于边缘设备上的简单对象跟踪，对象跟踪的输出可能很简单，就像在检测到的对象周围绘制一个边界框，在某些情况下，此类计算中使用的输入矩阵很小（例如4x7、3x3、5x5等）。结果，实际工作的数量很低。因此，典型的基于多线程的并行化技术无法加速跟踪应用程序。取而代之的是，基于吞吐量的平行化技术，每个线程在独立的视频序列上运行更有意义。在本文中，我们分享了同行在共享内存多项式上简单的在线和实时跟踪（排序）应用程序并行的经验。
Online and Real-time Object Tracking is an interesting workload that can be used to track objects (e.g., car, human, animal) in a series of video sequences in real-time. For simple object tracking on edge devices, the output of object tracking could be as simple as drawing a bounding box around a detected object and in some cases, the input matrices used in such computation are quite small (e.g., 4x7, 3x3, 5x5, etc). As a result, the amount of actual work is low. Therefore, a typical multi-threading based parallelization technique can not accelerate the tracking application; instead, a throughput based parallelization technique where each thread operates on independent video sequences is more rewarding. In this paper, we share our experience in parallelizing a Simple Online and Real-time Tracking (SORT) application on shared-memory multicores.
