Comprehensive Review of Machine Learning Techniques for Vehicle Tracking in A Smart Environment

Real-time vehicle tracking has become very important for managing traffic, keeping an eye on safety, and ‎letting cars find their own way, thanks to the fast growth of intelligent transportation systems (ITS) and ‎urban traffic surveillance. Deep learning techniques have made a big difference in the field by making ‎solutions that work in complicated, changing situations where traditional methods often fail. This work ‎looks at all the latest deep learning-based vehicle tracking methods, including single-object tracking ‎‎(SOT), multi-object tracking (MOT), and hybrid models that use both spatial and temporal data. In this ‎review, we look at more than 90 studies from 2020 to 2024, focusing on different types of structures like ‎CNNs, RNNs, and transformer-based models. The best tracking systems, such as DeepSORT, Byte Track, ‎FairMOT, Trans Track, and CNN-LSTM hybrids, are tested for their accuracy, computational efficiency, ‎ability to generalize datasets, and real-time performance. Problems like occlusion, identification switching, ‎and tracking drift are discussed, especially when there is a lot of traffic in a city. Lastly, the study points ‎out areas where more research is needed and suggests ways to go in the future to make vehicle tracking ‎systems that are flexible, light, and aware of their surroundings so they work best in smart cities.