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.

中文翻译：

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基于转移学习的集成深度神经网络用于足迹图像检索

作为犯罪现场必不可少的证据之一，足迹图像在连续案件的破译中不容忽视。传统的足迹比较和检索需要大量时间和人力，严重影响了案件的进展。随着深度学习的飞速发展，卷积神经网络在图像识别和检索方面表现出了优异的性能。为了满足公共安全足迹图像检索的实际需求，我们探索了卷积神经网络对足迹图像检索的影响，并提出了基于转移学习的集成深度神经网络用于图像检索。同时，基于边缘计算技术，我们开发了足迹采集系统来收集足迹数据。