Abstract
With the continuous improvement of the concept of grid marketing and service awareness, Whether the electricity hall staff is on the job has become the basic guarantee for monitoring service levels. In this paper, a self-updating and semi-supervised model is used to optimize the existing monitoring system. First of all, the combination of significant facial features and classical key point positioning has been used to construct feature extraction methods. And this method is used to extract the features of the random monitoring acquisition image, so as to realize the high frequency detection of “human” in the monitoring area. Subsequently, the person’s batch-acquisition image is used to identify his identity information within a fixed period. The feature extraction method is also used to extract features from standard marker images with the features of partial random monitoring acquisition image, which are used as input data for the semi-supervised k-NNM identification module. And the non-linear least-squares optimization algorithm was integrated to realize the weight distribution of the facial features. The above method builds a one to one recognition model. With the development of monitoring work, the correct identification sample data of the previous monitoring period is input into the model as a self-updated marking training sample to continuously improve the identification accuracy of the model. Under the condition of variable posture, the experiment shows that this method can meet the requirements of high timeliness for personnel on-the-job monitoring, and can also identify the appointed staff on the basis of facial features mining data in batches. This method can also update the sample to adapt to the change of electricity hall staff and provide non-staff warning.
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Tang, Y., Qiao, Z., Zou, R., Qiao, X., Liu, C., Wang, Y. (2018). Research on Monitoring Methods for Electricity Hall Staff Based on Autonomous Updating and Semi-supervising Model. In: Zhou, Q., Miao, Q., Wang, H., Xie, W., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 902. Springer, Singapore. https://doi.org/10.1007/978-981-13-2206-8_30
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DOI: https://doi.org/10.1007/978-981-13-2206-8_30
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