  Multi-pedestrian tracking in aerial imagery has several applications such as
large-scale event monitoring, disaster management, search-and-rescue missions,
and as input into predictive crowd dynamic models. Due to the challenges such
as the large number and the tiny size of the pedestrians (e.g., 4 x 4 pixels)
with their similar appearances as well as different scales and atmospheric
conditions of the images with their extremely low frame rates (e.g., 2 fps),
current state-of-the-art algorithms including the deep learning-based ones are
unable to perform well. In this paper, we propose AerialMPTNet, a novel
approach for multi-pedestrian tracking in geo-referenced aerial imagery by
fusing appearance features from a Siamese Neural Network, movement predictions
from a Long Short-Term Memory, and pedestrian interconnections from a GraphCNN.
In addition, to address the lack of diverse aerial pedestrian tracking
datasets, we introduce the Aerial Multi-Pedestrian Tracking (AerialMPT) dataset
consisting of 307 frames and 44,740 pedestrians annotated. We believe that
AerialMPT is the largest and most diverse dataset to this date and will be
released publicly. We evaluate AerialMPTNet on AerialMPT and KIT AIS, and
benchmark with several state-of-the-art tracking methods. Results indicate that
AerialMPTNet significantly outperforms other methods on accuracy and
time-efficiency.
