Revitalizing Electoral Trust: Enhancing Transparency and Efficiency through Automated Voter Counting with Machine Learning

TL;DR

This paper tackles the reliability and transparency challenges in manual vote counting by proposing automated voter counting via image processing using OpenCV, CVZone, and the MoG2 background-subtraction algorithm. The authors implement a three-phase methodology: literature review; a controlled voter-counting experiment; and data analysis contrasting automated counts against manually established ground truth, using metrics such as $F1$ score defined as $F1 = 2 \frac{Precision \times Recall}{Precision + Recall}$ with $Precision = \frac{TP}{TP+FP}$ and $Recall = \frac{TP}{TP+FN}$. The results demonstrate high performance, including entering counts with $100\%$ accuracy and overall accuracy around $99.15\%$ (average $F1$ around $0.9915$). The findings suggest that automated voting-counting can enhance efficiency, reduce human error, and rebuild public trust, with implications for scaling to national elections in regions with electoral challenges; however, limitations such as lighting sensitivity and occlusion point to the need for biometrics and cloud-based identity verification in future work.

Abstract

In order to address issues with manual vote counting during election procedures, this study intends to examine the viability of using advanced image processing techniques for automated voter counting. The study aims to shed light on how automated systems that utilize cutting-edge technologies like OpenCV, CVZone, and the MOG2 algorithm could greatly increase the effectiveness and openness of electoral operations. The empirical findings demonstrate how automated voter counting can enhance voting processes and rebuild public confidence in election outcomes, particularly in places where trust is low. The study also emphasizes how rigorous metrics, such as the F1 score, should be used to systematically compare the accuracy of automated systems against manual counting methods. This methodology enables a detailed comprehension of the differences in performance between automated and human counting techniques by providing a nuanced assessment. The incorporation of said measures serves to reinforce an extensive assessment structure, guaranteeing the legitimacy and dependability of automated voting systems inside the electoral sphere.

Figures (4)

• Figure 1: MOG2 background subtraction algorithm process
• Figure 2: All The Phases of the Evaluation Process.
• Figure 3: A comprehensive flow chart diagram.
• Figure 4: Dataset for voter counting.