  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.

Computer Vision and Pattern Recognition;Distributed, Parallel, and Cluster Computing