Multi-camera multi-vehicle tracking (MCMVT) is essential for intelligent transportation and urban security but is hindered by similar vehicle appearances, varying viewpoints, and occlusions. While existing offline algorithms offer high accuracy, their heavy computational load and lack of real-time performance limit practical use. To overcome these issues, this paper proposes an efficient online MCMVT method that improves tracking accuracy in complex environments and is suitable for edge devices. The method utilizes a lightweight YOLO11s detector to reduce detection time without compromising accuracy. The single-camera multi-vehicle tracking (SCMVT) algorithm is enhanced with a joint matching strategy based on cosine feature distances and IoU, combined with Exponential Moving Average (EMA)-based dynamic updates and small-target rejection, substantially boosting tracking accuracy and robustness. For cross-camera trajectory association, a hierarchical clustering algorithm based on cosine distance and Dunn’s index automatically optimizes clustering parameters to enhance matching precision. Additionally, leveraging SCMVT results maintains trajectory ID stability, reducing ID switching by 52% and lowering computational overhead. Experimental results demonstrate that the proposed method achieves an IDF1 score of 81.64 on the S02 scenario of the CityFlowV2 dataset with an average tracking time of 8 ms per frame and delivers real-time performance at 3 FPS on the NVIDIA Jetson AGX Orin edge device, confirming its efficiency and practicality.

In the contemporary landscape, the fusion of information technology and the rapid advancement of artificial intelligence have ushered school education into a transformative phase characterized by digitization and heightened intelligence. Concurrently, the global paradigm shift caused by the Covid-19 pandemic has catalyzed the evolution of e-learning, accentuating its significance. Amidst these developments, one pivotal facet of the online education paradigm that warrants attention is the authentication of identities within the digital learning sphere. Within this context, our study delves into a solution for online learning authentication, utilizing an enhanced convolutional neural network architecture, specifically the residual network model. By harnessing the power of deep learning, this technological approach aims to galvanize the ongoing progress of online education, while concurrently bolstering its security and stability. Such fortification is imperative in enabling online education to seamlessly align with the swift evolution of the educational landscape. This paper’s focal proposition involves the deployment of the YOLOv5 network, meticulously trained on our proprietary dataset. This network is tasked with identifying individuals’ faces culled from images captured by students’ open online cameras. The resultant facial information is then channeled into the residual network to extract intricate features at a deeper level. Subsequently, a comparative analysis of Euclidean distances against students’ face databases is performed, effectively ascertaining the identity of each student. It’s noteworthy that the innovative approach surpasses conventional YOLOv5 performance by offering specialized face detection, a critical function particularly relevant in the current pandemic milieu where face masks are commonplace. Furthermore, our methodology seamlessly extends the traditional YOLOv5 network’s capabilities.