Distributed Collaborative Object Retrieval with Blockchain-Based Edge Computing

In the current industrial informatics society, the numerous cameras deployed in the modern city promote the development of various video services, such as security monitoring and object retrieval. However, traditional methods encounter data leakage risks. Some camera owners are reluctant to share their data since the video contains confidential information. Meanwhile, domain diversities between cameras bring obstacles to practical object retrieval applications. To deal with these dilemmas, we propose a blockchain-based collaborative object retrieval (BCOR) system that can protect privacy as much as possible. BCOR includes two core components: multicamera reidentification framework (MC-ReF) and multicamera collaborative chain (M2C-Chain). Specifically, MC-ReF leverages visual relevance attention net (VRANet) to distinguish object identities in edge nodes. Through domain adaptation gradient optimization, VRANet can adapt to different cameras without the need for private camera data. M2C-Chain is responsible for maintaining the security and trust of the system. Through M2C-Chain, the collaboration among different nodes is transferred into a transaction-based manner, which is validated by a deeply integrated consensus. Finally, we implement a prototype system and deploy it into a real-world outdoor scene. The experiments indicate that BCOR achieves 30%-35% average improvement in domain adaptation on mean average precision and Rank-1 indicators. The performance analysis and security experiments also prove the efficiency and stability of BCOR.