Vision-based Vehicle Re-identification in Bridge Scenario using Flock
  Similarity
Chunfeng Zhang Ping Wang
Due to the needs of road traffic flow monitoring and public safety
management, video surveillance cameras are widely distributed in urban roads.
However, the information captured directly by each camera is siloed, making it
difficult to use it effectively. Vehicle re-identification refers to finding a
vehicle that appears under one camera in another camera, which can correlate
the information captured by multiple cameras. While license plate recognition
plays an important role in some applications, there are some scenarios where
re-identification method based on vehicle appearance are more suitable. The
main challenge is that the data of vehicle appearance has the characteristics
of high inter-class similarity and large intra-class differences. Therefore, it
is difficult to accurately distinguish between different vehicles by relying
only on vehicle appearance information. At this time, it is often necessary to
introduce some extra information, such as spatio-temporal information.
Nevertheless, the relative position of the vehicles rarely changes when passing
through two adjacent cameras in the bridge scenario. In this paper, we present
a vehicle re-identification method based on flock similarity, which improves
the accuracy of vehicle re-identification by utilizing vehicle information
adjacent to the target vehicle. When the relative position of the vehicles
remains unchanged and flock size is appropriate, we obtain an average relative
improvement of 204% on VeRi dataset in our experiments. Then, the effect of the
magnitude of the relative position change of the vehicles as they pass through
two cameras is discussed. We present two metrics that can be used to quantify
the difference and establish a connection between them. Although this
assumption is based on the bridge scenario, it is often true in other scenarios
due to driving safety and camera location.