# Evaluating Supervision Levels Trade-Offs for Infrared-Based People Counting

Source: [Evaluating Supervision Levels Trade-Offs for Infrared-Based People Counting](<https://www.sota2.com/research/paper/68fa713a1e209131cea4fe11>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:00:40.638Z

Authors: David Latortue, Moetez Kdayem, Fidel A Guerrero Pe\\~na, Eric Granger, Marco Pedersoli

Published: 2023-11-20T18:02:20.000Z

## Abstract

Object detection models are commonly used for people counting (and localization) in many applications but require a dataset with costly bounding box annotations for training. Given the importance of privacy in people counting, these models rely more and more on infrared images, making the task even harder. In this paper, we explore how weaker levels of supervision can affect the performance of deep person counting architectures for image classification and point-level localization. Our experiments indicate that counting people using a CNN Image-Level model achieves competitive results with YOLO detectors and point-level models, yet provides a higher frame rate and a similar amount of model parameters.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2311.11974>)

[PDF](<https://arxiv.org/pdf/2311.11974.pdf>)