Abstract
With the interconnection of roads spanning not only cities but also multiple countries, transportation capabilities have significantly improved. This has resulted in vehicles being able to reach far-off destinations within shorter timeframes. However, this progress poses challenges in identifying and tracking vehicles, particularly high-security or pursuit vehicles involved in crimes like theft, as they move across various security operational centers. Traditionally, centralized data access through traffic zones has been used for monitoring, surveillance, and tracking purposes. Nevertheless, this approach is susceptible to manipulation and compromises privacy due to unauthorized access by limited stakeholders. To address these concerns, a decentralized alternative is proposed, utilizing a blockchain network that ensures integrity, confidentiality, and access control for vehicle surveillance. Furthermore, computer vision algorithms can be employed for automated surveillance, enabling discreet re-identification or tracking of vehicles. This paper proposes an alternate framework that diverges from the centralized solution, instead encompassing multiple surveillance zones connected through a blockchain-based design with controlled access. To achieve this, we consider traditional approaches to vehicle re-identification using image similarity matching as opposed to resource-intensive unsupervised models performed on a federated consortium blockchain, the Hyperledger Fabric. The framework is evaluated based on latency, resource consumption, and the effectiveness of image processing algorithms for feature sharing through blockchain. Compared to existing frameworks with similar applications, our proposed framework demonstrates higher accuracy and shorter inference times while adhering to the specified constraints. It leverages the concept of blockchain in multi-surveillance zones to enhance privacy and security in automated vehicle surveillance.