  In this paper, we address various challenges in multi-pedestrian and vehicle
tracking in high-resolution aerial imagery by intensive evaluation of a number
of traditional and Deep Learning based Single- and Multi-Object Tracking
methods. We also describe our proposed Deep Learning based Multi-Object
Tracking method AerialMPTNet that fuses appearance, temporal, and graphical
information using a Siamese Neural Network, a Long Short-Term Memory, and a
Graph Convolutional Neural Network module for a more accurate and stable
tracking. Moreover, we investigate the influence of the Squeeze-and-Excitation
layers and Online Hard Example Mining on the performance of AerialMPTNet. To
the best of our knowledge, we are the first in using these two for a
regression-based Multi-Object Tracking. Additionally, we studied and compared
the L1 and Huber loss functions. In our experiments, we extensively evaluate
AerialMPTNet on three aerial Multi-Object Tracking datasets, namely AerialMPT
and KIT AIS pedestrian and vehicle datasets. Qualitative and quantitative
results show that AerialMPTNet outperforms all previous methods for the
pedestrian datasets and achieves competitive results for the vehicle dataset.
In addition, Long Short-Term Memory and Graph Convolutional Neural Network
modules enhance the tracking performance. Moreover, using
Squeeze-and-Excitation and Online Hard Example Mining significantly helps for
some cases while degrades the results for other cases. In addition, according
to the results, L1 yields better results with respect to Huber loss for most of
the scenarios. The presented results provide a deep insight into challenges and
opportunities of the aerial Multi-Object Tracking domain, paving the way for
future research.
