  In recent years, unmanned aerial vehicle (UAV) imaging is a suitable solution
for real-time monitoring different vehicles on the urban scale. Real-time
vehicle detection with the use of uncertainty estimation in deep meta-learning
for the portable platforms (e.g., UAV) potentially improves video understanding
in real-world applications with a small training dataset, while many vehicle
monitoring approaches appear to understand single-time detection with a big
training dataset. The purpose of real-time vehicle detection from oblique UAV
images is to locate the vehicle on the time series UAV images by using semantic
segmentation. Real-time vehicle detection is more difficult due to the variety
of depth and scale vehicles in oblique view UAV images. Motivated by these
facts, in this manuscript, we consider the problem of real-time vehicle
detection for oblique UAV images based on a small training dataset and deep
meta-learning. The proposed architecture, called SA-Net.v2, is a developed
method based on the SA-CNN for real-time vehicle detection by reformulating the
squeeze-and-attention mechanism. The SA-Net.v2 is composed of two components,
including the squeeze-and-attention function that extracts the high-level
feature based on a small training dataset, and the gated CNN. For the real-time
vehicle detection scenario, we test our model on the UAVid dataset. UAVid is a
time series oblique UAV images dataset consisting of 30 video sequences. We
examine the proposed method's applicability for stand real-time vehicle
detection in urban environments using time series UAV images. The experiments
show that the SA-Net.v2 achieves promising performance in time series oblique
UAV images.
