# Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis

arXiv: https://arxiv.org/abs/2007.16198 · HTML: https://mlsanity.com/paper/2007.16198/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [A Survey on Interpretable Reinforcement Learning](https://mlsanity.com/paper/2112.13112/) (2021)
- [The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting](https://mlsanity.com/paper/2207.09295/) (2022)
- [Counting Stacked Objects](https://mlsanity.com/paper/2411.19149/) (2024)
- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh](https://mlsanity.com/paper/2212.09144/) (2022)
- [Automated Counting of Stacked Objects in Industrial Inspection](https://mlsanity.com/paper/2603.15470/) (2026)
- [Computer vision based vehicle tracking as a complementary and scalable approach to RFID tagging](https://mlsanity.com/paper/2209.05911/) (2022)

## Abstract (arXiv)

The rapid advancement in the field of deep learning and high performance computing has highly augmented the scope of video based vehicle counting system. In this paper, the authors deploy several state of the art object detection and tracking algorithms to detect and track different classes of vehicles in their regions of interest (ROI). The goal of correctly detecting and tracking vehicles' in their ROI is to obtain an accurate vehicle count. Multiple combinations of object detection models coupled with different tracking systems are applied to access the best vehicle counting framework. The models' addresses challenges associated to different weather conditions, occlusion and low-light settings and efficiently extracts vehicle information and trajectories through its computationally rich training and feedback cycles. The automatic vehicle counts resulting from all the model combinations are validated and compared against the manually counted ground truths of over 9 hours' traffic video data obtained from the Louisiana Department of Transportation and Development. Experimental results demonstrate that the combination of CenterNet and Deep SORT, Detectron2 and Deep SORT, and YOLOv4 and Deep SORT produced the best overall counting percentage for all vehicles.