A Review of Deep Learning Approaches for Vehicle Classification and Tracking in Diverse Conditions

Vehicle classification and tracking are fundamental components of intelligent transportation systems (ITS), enabling real-time traffic monitoring, surveillance, and autonomous driving. This paper provides a comprehensive review of modern deep learning and artificial intelligence methods used in vehicle classification and tracking. It highlights key methods such as CNNs, YOLO, Faster R-CNN, Kalman Filters, DeepSORT, and LSTM networks, with a particular focus on hybrid CNN-LSTM models that combine spatial and temporal features for robust performance in dynamic scenes. The review identifies major challenges reported in the literature, including occlusion, adverse weather conditions, real-time processing requirements, and data privacy concerns. It also outlines the most common application scenarios, such as smart surveillance, urban traffic control, and autonomous navigation. Based on the current trends, the paper recommends future directions involving vision transformers, reinforcement learning, edge computing, and multimodal sensor fusion. The goal is to offer researchers and practitioners a structured overview of the state-of-the-art, while highlighting opportunities for improving adaptability, scalability, and efficiency in smart transportation systems.