Qureshi Asifa Mehmood, Alotaibi Moneerah, Alotaibi Sultan Refa, AlHammadi Dina Abdulaziz, Jamal Muhammad Asif, Jalal Ahmad, Lee Bumshik
Department of Computer Science, Air University, Islamabad, Pakistan.
Department of Computer Science, College of Science and Humanities, Shaqra University, Dawadmi, Saudi Arabia.
PeerJ Comput Sci. 2025 May 1;11:e2835. doi: 10.7717/peerj-cs.2835. eCollection 2025.
The high mobility of uncrewed aerial vehicles (UAVs) has led to their usage in various computer vision applications, notably in intelligent traffic surveillance, where it enhances productivity and simplifies the process. Yet, there are still several challenges that must be resolved to automate these systems. One significant challenge is the accurate extraction of vehicle foregrounds in complex traffic scenarios. As a result, this article proposes a novel vehicle detection and tracking system for autonomous vehicle surveillance, which employs Fuzzy C-mean clustering to segment the aerial images. After segmentation, we employed the YOLOv4 deep learning algorithm, which is efficient in detecting small-sized objects in vehicle detection. Furthermore, an ID assignment and recovery algorithm based on Speed-Up Robust Feature (SURF) is used for multi-vehicle tracking across image frames. Vehicles are determined by counting in each image to estimate the traffic density at different time intervals. Finally, these vehicles were tracked using DeepSORT, which combines the Kalman filter with deep learning to produce accurate results. Furthermore, to understand the traffic flow direction, the path trajectories of each tracked vehicle is projected. Our proposed model demonstrates a noteworthy vehicle detection and tracking rate during experimental validation, attaining precision scores of 0.82 and 0.80 over UAVDT and KIT-AIS datasets for vehicle detection. For vehicle tracking, the precision is 0.87 over the UAVDT dataset and 0.83 for the KIT-AIS dataset.
无人机（UAV）的高机动性使其在各种计算机视觉应用中得到了广泛应用，特别是在智能交通监控领域，它提高了工作效率并简化了流程。然而，要实现这些系统的自动化，仍有几个挑战需要解决。一个重大挑战是在复杂交通场景中准确提取车辆前景。因此，本文提出了一种用于自动驾驶车辆监控的新型车辆检测与跟踪系统，该系统采用模糊C均值聚类对航拍图像进行分割。分割后，我们采用了YOLOv4深度学习算法，该算法在车辆检测中检测小尺寸物体方面效率很高。此外，基于加速稳健特征（SURF）的ID分配与恢复算法用于跨图像帧的多车辆跟踪。通过对每张图像中的车辆进行计数来确定车辆数量，以估计不同时间间隔的交通密度。最后，使用DeepSORT对这些车辆进行跟踪，该方法将卡尔曼滤波器与深度学习相结合以产生准确的结果。此外，为了了解交通流方向，对每个跟踪车辆的路径轨迹进行了投影。我们提出的模型在实验验证期间展示了值得注意的车辆检测和跟踪率，在UAVDT和KIT - AIS数据集上进行车辆检测时，精度得分分别达到0.82和0.80。对于车辆跟踪，在UAVDT数据集上的精度为0.87，在KIT - AIS数据集上的精度为0.83。
