An Ensemble Deep Neural Network for Footprint Image Retrieval Based on Transfer Learning

As one of the essential pieces of evidence of crime scenes, footprint images cannot be ignored in the cracking of serial cases. Traditional footprint comparison and retrieval require much time and human resources, significantly affecting the progress of the case. With the rapid development of deep l...

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Bibliographic Details
Published in:Journal of sensors 2021, Vol.2021 (1)
Main Authors: Chen, Dechao, Chen, Yang, Ma, Jieming, Cheng, Cheng, Xi, Xuefeng, Zhu, Run, Cui, Zhiming
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Convolution
Crime
Datasets
Deep learning
Edge computing
Experiments
Human resources
Image databases
Image management
Image retrieval
Machine learning
Neural networks
Object recognition
R&D
Research & development
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Summary:As one of the essential pieces of evidence of crime scenes, footprint images cannot be ignored in the cracking of serial cases. Traditional footprint comparison and retrieval require much time and human resources, significantly affecting the progress of the case. With the rapid development of deep learning, the convolutional neural network has shown excellent performance in image recognition and retrieval. To meet the actual needs of public security footprint image retrieval, we explore the effect of convolution neural networks on footprint image retrieval and propose an ensemble deep neural network for image retrieval based on transfer learning. At the same time, based on edge computing technology, we developed a footprint acquisition system to collect footprint data. Experimental results on the footprint dataset we built show that our approach is useful and practical.
ISSN:1687-725X
1687-7268
DOI:10.1155/2021/6631029