ORCID
- Khan, Asiya: 0000-0003-3620-3048
- Sharma, Sanjay: 0000-0002-5062-3199
Abstract
Pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way that avoids traffic-accidents, which may result in individuals being harmed. In this work, a review of the convolutional neural networks (CNN) to tackle pedestrian detection is presented. We further present models based on CNN and transfer learning. The CNN model with the VGG-16 architecture is further optimised using the transfer learning approach. This paper demonstrates that the use of image augmentation on training data can yield varying results. In addition, a pre-processing system that can be used to prepare 3D spatial data obtained via LiDAR sensors is proposed. This pre-processing system is able to identify candidate regions that can be put forward for classification, whether that be 3D classification or a combination of 2D and 3D classifications via sensor fusion. We proposed a number of models based on transfer learning and convolutional neural networks and achieved over 98% accuracy with the adaptive transfer learning model.
DOI
10.3390/electronics10243159
Publication Date
2021-12-18
Publication Title
Electronics
Volume
10
Issue
24
Embargo Period
2022-02-10
Organisational Unit
School of Engineering, Computing and Mathematics
First Page
3159
Last Page
3159
Recommended Citation
Mounsey, A., Khan, A., & Sharma, S. (2021) 'Deep and Transfer Learning Approaches for Pedestrian Identification and Classification in Autonomous Vehicles', Electronics, 10(24), pp. 3159-3159. Available at: https://doi.org/10.3390/electronics10243159
