A Comprehensive Review of Transformer Based, Deep Learning, and Sensor Driven Approaches for Multi Type Vehicle Detection, Tracking, Counting, and Traffic Intelligence in Complex Traffic Environments
Year: 2026
Type: article
Abstract: The rapid growth of urban traffic has led to increasing demand for intelligent, automated systems that can efficiently detect, track, and count multiple types of vehicles in real-time. Traditional sensor-based solutions are steadily being replaced by powerful AI-driven frameworks integrating deep learning, LiDAR analysis, stereo vision, Wi-Fi CSI, and transformer-based global attention architectures. Over the past decade, vehicle detection, tracking, and counting have evolved from simple background-subtraction mechanisms to sophisticated deep learning pipelines incorporating convolutional neural networks, transformer-based architectures, multimodal sensor fusion, and context-aware global feature modelling. This paper provides a comprehensive review of state-of-the-art methods, combining insights from modern CNN-based detectors such as YOLO, Retina Net, R-CNN, and transformer pipelines including DETR enhanced with residual global attention. Additional discussions include LiDAR-supported maneuverer classification, KLT-based motion analysis, shallow feature-fusion tracking models, and privacy-aware passenger estimation using wireless signals. Security concerns related to adversarial robustness, sensor spoofing, and data integrity are also addressed to ensure safe deployment in Intelligent Transportation Systems.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7119514175
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