Abstract:
Intelligent video surveillance systems are becoming an increasingly
important major area of artificial intelligence as the use of motor
vehicles in today’s transportation networks grows. In recent times
re-identifying vehicles over a surveillance camera network have been
proven to be the most effective method of efficiently controlling traf fic, upholding the law, gathering information, and programming the
traffic. Vehicle Re-ID seeks to recognize a target vehicle in sev eral, non-overlapping camera perspectives. Compared to the com mon person re-ID issue, it has gotten far less attention in the com puter vision community. The absence of pertinent research data and
the unique 3D structure of a vehicle are two potential causes of this
delayed progression. The major concerns are the modest inter-class
disparity caused by almost similar identities and the large intra-class
distance based on a variety of circumstances, such as background in terference, illumination, and viewpoints. In the past, the majority
of the works focused on particular viewpoints (e.g. front and rear
only) however, actual situations demonstrate that these methods are
ineffective, as vehicles typically appeared from various perspectives
to cameras. To this end, we present PAK Vehicle Re-ID Dataset (PV-ReID) with arbitrary viewpoints including unique vehicle iden tities based on Asian regions like Pakistan and India. In addition,
we present a generalized pipeline for vehicle re-identification sys tems, which achieved 84.6%, 93.4%, and 78.3% accuracy, respec tively. Lastly, a comprehensive comparison has been carried out to
demonstrate the effectiveness of our proposed dataset and vehicle
re-identification pipeline.
