Building a People Counting Solution Using Computer Vision

EasyChair Preprint 12521

Abstract

The development and implementation of people counting solutions using computer vision have gained significant attention in various domains, including retail, transportation, and public spaces. This paper presents an overview of the process involved in building a people counting solution using computer vision techniques. The proposed solution aims to accurately count the number of people in a given area by leveraging the capabilities of computer vision algorithms and machine learning models.

The paper begins by discussing the importance of people counting in different applications, such as crowd management, resource allocation, and customer behavior analysis. It highlights the limitations of traditional manual counting methods and emphasizes the need for automated and reliable solutions.

Next, the paper delves into the technical aspects of building a people counting system. It provides a comprehensive overview of computer vision techniques, including object detection, tracking, and image segmentation, which form the foundation of the proposed solution. The integration of machine learning algorithms, such as convolutional neural networks (CNNs), for accurate people detection and tracking is also explored.

Furthermore, the paper discusses the data acquisition process, including camera selection, placement, and calibration. It addresses challenges such as occlusions, lighting conditions, and camera perspective, which can impact the accuracy of people counting.

Keyphrases: computer, counting, vision

@booklet{EasyChair:12521,
  author    = {Favour Olaoye and Kaledio Potter},
  title     = {Building a People Counting Solution Using Computer Vision},
  howpublished = {EasyChair Preprint 12521},
  year      = {EasyChair, 2024}}