极小矩阵的在线实时的目标跟踪算法
Online and Real-time Object Tracking Algorithm with Extremely Small Matrices
摘要：
在线和实时的【形】对象跟踪是一种有趣的工作负载，可用于跟踪对象（例如，汽车、人、动物）在一系列视频序列中实时的【形】。对于边缘设备上的简单对象跟踪，对象跟踪的输出可以像在检测到的对象周围绘制限界框一样简单，并且在某些情况下，在这种计算中使用的输入矩阵非常小（例如，4x 7、3x 3、5x 5等）。因此，实际工作量很低。因此，典型的基于多线程的并行化技术不能加速跟踪应用;相反，基于吞吐量的并行化技术，其中每个线程对独立的视频序列进行操作是更有益的。在本文中，我们分享我们的经验，在共享内存多核并行化的简单在线和实时的【形】跟踪（SORT）应用程序。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
