Cascade Classifiers and Saliency Maps Based People Detection

  • Wilbert G. AguilarEmail author
  • Marco A. Luna
  • Julio F. Moya
  • Vanessa Abad
  • Hugo Ruiz
  • Humberto Parra
  • William Lopez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10325)


In this paper, we propose algorithm and dataset for pedestrian detection focused on HCI and Augmented Reality applications. We combine cascade classifiers with saliency maps for improving the performance of the detectors. We train a HAAR-LBP and HOG cascade classifier and introduce CICTE_PeopleDetection dataset with images from surveillance cameras at different angles and altitudes. Our algorithm performance is compared with other approaches from the state of art. In the results, we can see that cascade classifiers with saliency maps improve the performance of pedestrian detection due to the rejection of false positives in the image.


HAAR HOG LBP Saliency maps People detection Cascade classifiers HCI 



This work is part of the projects VisualNavDrone 2016-PIC-024 and MultiNavCar 2016-PIC-025, from the Universidad de las Fuerzas Armadas ESPE, directed by Dr. Wilbert G. Aguilar.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wilbert G. Aguilar
    • 1
    • 3
    • 4
    Email author
  • Marco A. Luna
    • 2
  • Julio F. Moya
    • 2
  • Vanessa Abad
    • 5
  • Hugo Ruiz
    • 1
    • 6
  • Humberto Parra
    • 1
    • 7
  • William Lopez
    • 3
  1. 1.Dep. Seguridad y DefensaUniversidad de las Fuerzas Armadas ESPESangolquíEcuador
  2. 2.Dep. Eléctrica y ElectrónicaUniversidad de las Fuerzas Armadas ESPESangolquíEcuador
  3. 3.CICTE Research CenterUniversidad de las Fuerzas Armadas ESPESangolquíEcuador
  4. 4.GREC Research GroupUniversitat Politècnica de CatalunyaBarcelonaSpain
  5. 5.Universitat de BarcelonaBarcelonaSpain
  6. 6.PLM Research CenterPurdue UniversityWest LafayetteUSA
  7. 7.Universidad Politécnica de MadridMadridSpain

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