Evaluating Supervision Levels Trade-Offs for Infrared-Based People
  Counting
David Latortue Moetez Kdayem Fidel A Guerrero Peña Eric Granger Marco Pedersoli
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.