Abstract
Food security is related to the national economy and people’s livelihood. The grain storage security is the key to achieve food security, therefore, relevant departments have attached great importance to the traceability of the warehousing link. In this paper, we use R-CNN algorithm to extract the target information related to traceability from monitoring videos in the warehouse, then match the targets in adjacent frame based on the class, location and image feature, and finally connect the same target in the adjacent frames to obtain the running track of the target in current monitoring scene. It can be seen from the experimental results that the target detection and recognition algorithm based on R-CNN can reach a higher level in recognition accuracy and detection rate, which meets the needs of real-time analysis. At the same time, the multi-factor target matching fusion algorithm can balance the matching accuracy and matching efficiency, and is a relatively better target matching method. Trajectory data extracted based on the above data can directly reflect the running route of each target. It is also easier for the public to accept such data as the evidence and basis for traceability.
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Acknowledgements
This work was supported by China Special Fund for Grain-scientific Research in the Public Interest (201513004), National Science Foundation of China (61403188), Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (14KJA520001, 16KJA170003, 15KJA120001), and the Youth Foundation of Nanjing Institute of Technology (QKJ201803).
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Zhang, J., Li, B., Sun, J., Mao, B., Lu, A. (2020). Extraction Method of Traceability Target Track in Grain Depot Based on Target Detection and Recognition. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_53
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