Object tracking is an important functionality of edge video analytic systems and services. Multi-object tracking (MOT) detects the moving objects and tracks their locations frame by frame as real scenes are being captured into a video. However, it is well known that real time object tracking on the edge poses critical technical challenges, especially with edge devices of heterogeneous computing resources. This paper examines the performance issues and edge-specific optimization opportunities for object tracking. We will show that even the well trained and optimized MOT model may still suffer from random frame dropping problems when edge devices have insufficient computation resources. We present several edge specific performance optimization strategies, collectively coined as EMO, to speed up the real time object tracking, ranging from window-based optimization to similarity based optimization. Extensive experiments on popular MOT benchmarks demonstrate that our EMO approach is competitive with respect to the representative methods for on-device object tracking techniques in terms of run-time performance and tracking accuracy. EMO is released on Github at https://github.com/git-disl/EMO.

翻译：目标跟踪是边缘视频分析系统与服务的重要功能。多目标跟踪（MOT）在真实场景被捕捉为视频时，逐帧检测移动目标并追踪其位置。然而，众所周知，在边缘设备上实现实时目标跟踪存在关键技术挑战，尤其是对于具有异构计算资源的边缘设备而言。本文研究了目标跟踪中的性能问题及边缘特定的优化机会。我们将证明，即使是训练良好且经过优化的MOT模型，在边缘设备计算资源不足时仍可能面临随机丢帧问题。我们提出了几种边缘特定的性能优化策略，统称为EMO，以加速实时目标跟踪，涵盖从基于窗口的优化到基于相似性的优化。在主流MOT基准上的大量实验表明，我们的EMO方法在运行时性能和跟踪精度方面，与用于设备端目标跟踪的代表性方法相比具有竞争力。EMO已在GitHub上开源，地址为https://github.com/git-disl/EMO。