Active shooter detection and robust tracking utilizing supplemental
  synthetic data
Joshua R. Waite Jiale Feng Riley Tavassoli Laura Harris Sin Yong Tan Subhadeep Chakraborty Soumik Sarkar
The increasing concern surrounding gun violence in the United States has led
to a focus on developing systems to improve public safety. One approach to
developing such a system is to detect and track shooters, which would help
prevent or mitigate the impact of violent incidents. In this paper, we proposed
detecting shooters as a whole, rather than just guns, which would allow for
improved tracking robustness, as obscuring the gun would no longer cause the
system to lose sight of the threat. However, publicly available data on
shooters is much more limited and challenging to create than a gun dataset
alone. Therefore, we explore the use of domain randomization and transfer
learning to improve the effectiveness of training with synthetic data obtained
from Unreal Engine environments. This enables the model to be trained on a
wider range of data, increasing its ability to generalize to different
situations. Using these techniques with YOLOv8 and Deep OC-SORT, we implemented
an initial version of a shooter tracking system capable of running on edge
hardware, including both a Raspberry Pi and a Jetson Nano.