Automatic detection of people in aerial images has potential applications in traffic monitoring, surveillance, human behavior analysis, etc. However, developing an algorithm for detection of human locations in aerial images is challenging because of the small target size, cluttered background, and varying appearance of humans. Deep learning-based object detections frameworks internally use the standard convolutional neural network (CNN) based classifiers for feature extraction and classification. Though these pre-trained classifiers perform image classification tasks with very good accuracy, they are computationally complex and hence require huge computation time. In this work, we custom-designed CNN-based classifiers to perform the human classification in aerial images and compared the performance with the standard VGG-16 based human classifier. Custom-designed classifier with fewer number of layers achieved a reduced computation time while maintaining good accuracy.

Original languageEnglish
Title of host publicationMachine Learning and Autonomous Systems - Proceedings of ICMLAS 2021
EditorsJoy Iong-Zong Chen, Haoxiang Wang, Ke-Lin Du, V. Suma
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9789811679957
Publication statusPublished - 2022
EventInternational Conference on Machine Learning and Autonomous Systems, ICMLAS 2021 - Kanyakumari, India
Duration: 24-09-202125-09-2021

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026


ConferenceInternational Conference on Machine Learning and Autonomous Systems, ICMLAS 2021

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Computer Science(all)


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