Abstract
Aiming at the problem of automatic detection of normal operation behavior in self-service business management, with improved motion history image as input, a recognition method of convolutional neural network is proposed to timely judge the occurrence of anomie behavior. Firstly, the key frame sequence was extracted from the self-service operation video based on the method of uniform energy down-sampling. Secondly, combined with the timing information of key frames to adaptively estimate the decay parameters of the motion history image, adding information contrast to generating a logic matrix can improve the calculation speed of the improved motion history image. Finally, the formed motion history image was input into the established convolutional neural network to obtain the class of self-service behavior and distinguish anomie behavior. In real scenarios of self-service baggage check-in for civil aviation passengers, the typical check-in behavior data set is established and tested in actual self-service baggage check-in system of the airport. The results show that the method proposed can effectively identify typical anomie behaviors and has high practical value.
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Deng, L., Gao, Q., Xu, D. (2020). Self-service Behavior Recognition Algorithm Based on Improved Motion History Image Network. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_34
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DOI: https://doi.org/10.1007/978-981-15-7981-3_34
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