智慧园区环境下的多模态多核学习身份识别算法研究

Research on multi-modal and multi-kernel learning identity recognition algorithm in smart parks

智慧园区的建设推动着企业与城市的发展,传统的园区管理方式已不再适用于产业融合创新的智慧园区。以曹家滩园区为例,设计智慧园区平台总体框架,针对园区中身份识别存在识别环境差、效率低、准确率低等问题,提出一种基于多模态多核学习的身份识别算法。所提算法将视频数据中的数据分为图像、音频,并采集个人信息的文本,并将三种模态的信息输入同一样本空间中,通过引入间隔约束的多核学习算法,保留不同模态的差异性和相似性,并进行特征融合与决策融合,最终采用分类器与评分机制输出身份识别结果。通过公开的视频数据集与曹家滩园区数据集进行实验,实验结果表明本文所提算法最高准确率达到97.2%,与传统算法相比有较大优势。

The construction of smart parks promotes the development of enterprises and cities, and traditional park management methods are no longer suitable for smart parks with industrial integration and innovation. This paper takes Caojiatan Park as an example to design the overall framework of the smart park platform. Aiming at the problems of poor recognition environment, low efficiency and low accuracy in the park’s identity recognition, this paper proposes an identity recognition algorithm based on multi-modal and multi-kernel learning. The proposed algorithm divides the data in the video data into images and audio, and collects the text of personal information, and inputs the information of the three modalities into the same sample space. By introducing a multi-kernel learning algorithm with interval constraints, the difference is retained to the greatest extent. The difference and similarity of modalities are combined with feature fusion and decision fusion, and finally the classifier and scoring mechanism are used to output the identification results. Through experiments on the public video dataset and Caojiatan Park dataset, the experimental results show that the algorithm proposed in this paper has a maximum accuracy of 97.2%, which has a great advantage over traditional algorithms.