Blockchain-Empowered Distributed Multicamera Multitarget Tracking in Edge Computing

The rapid increase in the volume of video data generated from edges in the Industrial Internet of Things, opens up new possibilities for enhancing the application of video service. Multicamera multiobject tracking (MCMT) has always been a fundamental task in video surveillance or traffic control. However, the traditional MCMT methods are limited by the communication bottleneck and computation resources of the centralized curator, and suffer from security and privacy issues. In this article, we first design multicamera multihypothesis tracking (MC-MHT) framework to achieve real-time tracking performance among edge cameras. The complex association of objects is described by multiskip trees. The tracking task is well distributed to each camera. Then, we integrate multicamera tracking chain into MC-MHT to ensure security and trust. The state transition of targets in multicamera is illustrated from the perspective of blockchain transactions. The transactions are validated by an integrated tracking consensus to counter Byzantine behavior. Numerical results derived from real-world scenarios and CAMPUS dataset show that the proposed method achieves real-time performance (24-36 FPs) and 79.0-82.4 MOTA indicator, as well as reduces identity switch errors about 71% under Byzantine attack.