Collaborative Video Processing of Multiple Cameras in Smart Transportation: Content Analysis and Resource Allocation

In the context of smart transportation, the collaborative processing of video data sourced from multiple cameras plays a pivotal role in promoting efficient traffic management and augmenting safety measures. Nevertheless, the exponential surge in surveillance cameras deployment has concurrently engendered a rapid increase in the magnitude of video analysis tasks and data volume. To address these challenges, we propose a comprehensive framework for collaborative video processing. Primarily, a collaborative content analysis approach is proposed, and which employs a Transformer-based ReID (Re-identification) algorithm to construct key stickers. These key stickers are optimized with cross-cameras correlations and serve as the foundational structure for subsequent online video compression. Subsequently, we propose a collaborative resource allocation approach, and which involves the formulation of a queue model designed for the orchestration of online camera analysis tasks. In addition, we have devised an enhanced deep reinforcement learning algorithm to fine-tune the task scheduling configuration of multiple cameras, with guidance from the queue model. Extensive experiments and simulations were conducted to evaluate the proposed framework. The results demonstrate its effectiveness in achieving accurate and real-time analysis of video data in smart transportation scenarios.