Interactive Siamese Network-Based Roadside Perception for Multi-Vehicle Tracking

Roadside perception has a wider sensing range than onboard detection, providing enhanced sensory information for intelligent transportation systems, thus gaining increasing attention in recent years. However, directly applying onboard perception algorithms on roadside detectors (RSD) is infeasible due to the challenging requirements for high maneuvering target tracking and discriminating highly similar targets. Therefore, an Interactive Siamese Network (ISN) is proposed in this paper to overcome the roadside perception difficulties. Specifically, an interactive similarity contrast encoder-decoder has been developed within the ISN tracker. The sensitivity of algorithm to rapid changes in vehicle states is enhanced through the adaptive adjustment of weights assigned to critical tracking parameters within the loss function. This strategy enhances the ISN tracking effect for long-term trajectories for high maneuvering driving. Then, a global trajectory optimization unit is integrated into the ISN tracker. A trajectory similarity threshold is established to conduct cross-association analysis on similar trajectories, followed by iterative operations on adjacent trajectories. This approach further enhances the resolution of similar trajectories while ensuring the optimality of global trajectories. The proposed method is evaluated on the DAIR-V2X dataset and compared against current state-of-the-art methods. The experimental results verify that the proposed method provides efficient and accurate estimations and tracks vehicles in the intersection area, affording better accuracy and recall rate in high maneuvering target tracking than existing methods.