  As a large proportion of road accidents occur at intersections, monitoring
traffic safety of intersections is important. Existing approaches are designed
to investigate accidents in lane-based traffic. However, such approaches are
not suitable in a lane-less mixed-traffic environment where vehicles often ply
very close to each other. Hence, we propose an approach called Siamese
Interaction Long Short-Term Memory network (SILSTM) to detect collision prone
vehicle behavior. The SILSTM network learns the interaction trajectory of a
vehicle that describes the interactions of a vehicle with its neighbors at an
intersection. Among the hundreds of interactions for every vehicle, there maybe
only some interactions which may be unsafe and hence, a temporal attention
layer is used in the SILSTM network. Furthermore, the comparison of interaction
trajectories requires labeling the trajectories as either unsafe or safe, but
such a distinction is highly subjective, especially in lane-less traffic.
Hence, in this work, we compute the characteristics of interaction trajectories
involved in accidents using the collision energy model. The interaction
trajectories that match accident characteristics are labeled as unsafe while
the rest are considered safe. Finally, there is no existing dataset that allows
us to monitor a particular intersection for a long duration. Therefore, we
introduce the SkyEye dataset that contains 1 hour of continuous aerial footage
from each of the 4 chosen intersections in the city of Ahmedabad in India. A
detailed evaluation of SILSTM on the SkyEye dataset shows that unsafe
(collision-prone) interaction trajectories can be effectively detected at
different intersections.
