Abstract
On the basis of the shoe prints left by the suspects at the crime scene, it can be inferred that the specific type of shoes worn by the suspects, and then the type of suspected shoes can be searched in the monitoring around the crime scene, which is a common investigative technique used by public security organs. However, this technique is less automated and intelligent, and in most cases, the shoes under video monitoring are small and mostly fuzzy. An automatic classification method of footwear for pedestrians under low resolution video monitoring is proposed. A footwear database has been constructed with 149,199 footwear images; Then, based on the convolutional neural network, a network model suitable for automatic footwear classification is designed. The experimental results show that the accuracy of the automatic footwear classification network model in the test stage is up to 98.47%.
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Acknowledgements
This work is supported by National Key Research and Development Program of China (Grant No. 2017YFC0822003), the National Natural Science Foundation of China (Grant No. 61503387), the Opening Project of Shanghai Key Laboratory of Crime Scene Evidence.
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Jiang, H., Yang, M., Mi, Z., Tang, Y. (2019). Research on Automatic Classification Method of Footwear Under Low Resolution Condition. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_48
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DOI: https://doi.org/10.1007/978-3-030-31456-9_48
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