Trending Machine Learning Methods for Vehicle, Pedestrian, and Traffic for Detection and Tracking Task in the Post-Covid Era: A Literature Review

This study, aimed at professionals in research and development in the fields of computer vision, artificial intelligence, and intelligent transportation, presents a systematic literature review on recent machine learning methodologies applied to the detection and tracking of vehicles, pedestrians, and traffic flow. The analysis of articles published between 2022 and 2025 (early access) in the post-COVID era explored the integration of machine learning and deep learning to address traffic challenges, allowing for the comparison of different approaches and the formulation of hypotheses based on the 46 articles that comprised the review corpus. Furthermore, the evaluation of the reported metrics revealed inconsistencies in the methodologies employed, attributed to the lack of standardization across the studies. In light of this, this work proposes alternatives for future experiments, emphasizing the emerging potential of the field through the adoption of new standardization systems and the exploration of experimental combinations.