  A lot a research is focused on object detection and it has achieved
significant advances with deep learning techniques in recent years. Inspite of
the existing research, these algorithms are not usually optimal for dealing
with sequences or images captured by drone-based platforms, due to various
challenges such as view point change, scales, density of object distribution
and occlusion. In this paper, we develop a model for detection of objects in
drone images using the VisDrone2019 DET dataset. Using the RetinaNet model as
our base, we modify the anchor scales to better handle the detection of dense
distribution and small size of the objects. We explicitly model the channel
interdependencies by using "Squeeze-and-Excitation" (SE) blocks that adaptively
recalibrates channel-wise feature responses. This helps to bring significant
improvements in performance at a slight additional computational cost. Using
this architecture for object detection, we build a custom DeepSORT network for
object detection on the VisDrone2019 MOT dataset by training a custom Deep
Association network for the algorithm.
