Automatic Annotation for Weakly Supervised Pedestrian Detection

• Francisco J. Garcia-Espinosa ^[1] ; Antonio S. Montemayor ^[1] ; Alfredo Cuesta-Infante ^[1]
  1. [1] Universidad Rey Juan Carlos

     Universidad Rey Juan Carlos

     Madrid, España

     
• Localización: Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence: 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Puerto de la Cruz, Tenerife, Spain, May 31 – June 3, 2022, Proceedings, Part II / José Manuel Ferrández Vicente (dir. congr.), José Ramón Álvarez Sánchez (dir. congr.), Félix de la Paz López (dir. congr.), Hojjat Adeli (aut.), 2022, ISBN 978-3-031-06527-9, págs. 308-317
• Idioma: inglés
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• Resumen

  • Pedestrian detection is an important task addressed in computer vision given its direct application in video surveillance, autonomous driving and biomechanics among many others. The advent of deep neural networks has meant a breakthrough in its resolution. The major problem is the need for very large labeled datasets, which is usually difficult to obtain, either because it is not publicly available or it is not suitable for the particular problem. To solve it, we design a method capable of self-labeling a detection dataset using only small manually labeled portion of it. Results show an autolabeled dataset of 10342 images from a preliminary set of 1312 manually labeled images.