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.
翻译：在线和实时天体跟踪是一个有趣的工作量,可用于实时跟踪一系列视频序列中的物体(如汽车、人、动物等)。对于在边缘设备上简单的天体跟踪而言,天体跟踪的输出可以简单到在被检测到的物体周围绘制一个捆绑框,在某些情况下,这种计算中所使用的输入矩阵非常小(如4x7、3x3、5x5等)。因此,实际工作的数量很低。因此,典型的基于平行的多读技术无法加速跟踪应用;相反,基于平行的吞吐技术,即每个线条在独立视频序列上运行的平行技术更有意义。在本文中,我们分享了在共享式多芯上平行进行简单在线实时跟踪(SORT)应用的经验。
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