Study of Haar-AdaBoost (VJ) and HOG-AdaBoost (PoseInv) Detectors for People Detection

Object detection in general and pedestrians in particular in images and videos is a very popular research topic within the computer vision community; it is an issue that is currently at the heart of much research. The detection of people is a particularly difficult subject because of the great variability of appearances and the situations in which a person may find themselves (a person is not a rigid object; it is articulate and unpredictable; its shape changes during its movement). The situations in which a person may find themselves are very varied: They are alone, near a group of people or in a crowd, obscured by an object. In addition, the characteristics vary from one person to another (color of the skin, hair, clothes, etc.), the background simple, clear or complex, the lighting or weather conditions, the shadow caused by different light sources, etc. greatly complicate the problem. In this article, we will present a comparative study of the performance of the two detectors Haar-AdaBoost and HOG-AdaBoost in detecting people in the INRIA images database of persons. An evaluation of the experiments will be presented after making certain modifications to the detection parameters.

@article{TALEB2021,
  title     = {Study of Haar-AdaBoost (VJ) and HOG-AdaBoost (PoseInv) Detectors for People Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {12},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Nagi OULD TALEB and Mohamed Larbi BEN MAATI and Mohamedade Farouk NANNE and Aicha Mint Aboubekrine and Adil CHERGUI},
  doi       = {10.14569/IJACSA.2021.0121266},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121266}
}