TY - GEN
T1 - Human Classification in Aerial Images Using Convolutional Neural Networks
AU - Akshatha, K. R.
AU - Karunakar, A. K.
AU - Satish Shenoy, B.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85125288277
UR - https://www.scopus.com/pages/publications/85125288277#tab=citedBy
U2 - 10.1007/978-981-16-7996-4_39
DO - 10.1007/978-981-16-7996-4_39
M3 - Conference contribution
AN - SCOPUS:85125288277
SN - 9789811679957
T3 - Smart Innovation, Systems and Technologies
SP - 537
EP - 549
BT - Machine Learning and Autonomous Systems - Proceedings of ICMLAS 2021
A2 - Chen, Joy Iong-Zong
A2 - Wang, Haoxiang
A2 - Du, Ke-Lin
A2 - Suma, V.
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Machine Learning and Autonomous Systems, ICMLAS 2021
Y2 - 24 September 2021 through 25 September 2021
ER -