View source for Projects:2019s1-111 Deep Learning-based Object Detection and Tracking of Moving Targets from a Drone


== Introduction ==

The aim of this project is to determine the performance of state-of-the-art deep learning object detection algorithms to detect and track a moving target from a fast moving drone.  

The motivation behind this project is the need for moral weapons to become more prevalent in the defence force. 

In July 2015 an open letter with signatories including Elon Musk, Steve Wozniak and the late Stephen Hawking asking for a ban on autonomous weapons was released. In summary the letter asks for a ban on offensive autonomous weapons beyond meaningful human control. In addition to this, in 2013 the UN presented a report with recommendations for testing, production and deployment of LARs (Lethal Autonomous Robotics).  

An example of why many world leading academics feel so passionately about this issue is the Grdelica train bombing disaster which occurred in 1999 in Serbia.  2 missiles were fired by a NATO aircraft with the aim to bomb the bridge. The pilot claims to have not seen the passenger training and unfortunately hit the train killing up to 60 people.  By implementing moral weapons, an autonomous loitering weapon (e.g., drone) it will be able to detect, identify and tracks key ground objects near and around a target to avoid events such as the Grdelica disaster.  

 
== Supervisors ==

Dr Cheng-Chew Lim
Sponsored by DSTG