Urban traffic management (UTM) relies on accurate vehicular flow data to optimize infrastructure and reduce congestion. However, existing video-based methods struggle with occlusions and complex trajectories in multi-directional intersections, limiting their applicability. This paper presents a novel Speed Calculation and Trajectory Observation Algorithm (SCTOA) for robust vehicle counting, classification, and speed estimation in visually obstructed urban environments (e.g., tunnels, overpasses). Our approach integrates drone-captured aerial footage with YOLOv8 detection, ByteTrack tracking, and LoFTR-based motion compensation. To address occlusion challenges, we propose strategically placed Observation Areas (OAs), in points of traffic bifurcation, improving the overall performance. By mapping vehicle trajectories between OAs we achieve up to 96.73% accuracy on vehicle counting in unobstructed zones and 87.8% in complex scenarios, —outperforming prior single-direction methods. Experimental validation across 27 hours of drone footage from high-traffic intersections demonstrates the efficacy of the method for multi-directional flow analysis. The results enable precise input for traffic simulators (e.g., PTV-Vissim), supporting data-driven UTM decisions while minimizing costly real-world experimentation.

In this paper we propose a novel set of techniques for real-time Multi-Target Multi-Camera (MTMC) tracking of vehicles in congested, low speed environments, such as those of drive-thru scenarios, where metrics such as the number of vehicles, time of stay, and interactions between vehicles and staff are needed and must be highly accurate. Traditional methods of tracking based on Intersection over Union (IoU) and basic appearance features produce fragmented trajectories of misidentifications under these conditions. Furthermore, detectors, such as YOLO (You Only Look Once) architectures, exhibit different types of errors due to vehicle proximity, lane changes, and occlusions. Our methodology introduces a new tracker algorithm, Multi-Object Tracker based on Corner Displacement (MTCD), that improves the robustness against bounding box deformations by analysing corner displacement patterns and several other factors involved. The proposed solution was validated on real-world drive-thru footage, outperforming standard IoU-based trackers like Nvidia Discriminative Correlation Filter (NvDCF) tracker. By maintaining accurate cross-camera trajectories, our framework enables the extraction of critical operational metrics, including vehicle dwell times and person–vehicle interaction patterns, which are essential for optimizing service efficiency. This study tackles persistent tracking challenges in constrained environments, showcasing practical applications for real-world surveillance and logistics systems where precision is critical. The findings underscore the benefits of incorporating geometric resilience and delayed decision-making into MTMC architectures. Furthermore, our approach offers the advantage of seamless integration with existing camera infrastructure, eliminating the need for new deployments.