As easy-to-search semantic information, human clothing attributes have important research value in the field of computer vision. Existing attribute recognition methods encounter problems such as interference from environmental factors, and as a result show poor clothing positioning accuracy. To address these problems, a human attribute recognition method based on human pose estimation and multiple-feature fusion is proposed. First, some retrieval results are obtained for subsequent attribute recognition through appearance feature matching. Then, through a deep SSD-based human pose estimation method, the foreground area belonging to the human in the image is located, and the background interference is excluded. Finally, the analytical results of various methods are combined. The iterative smoothing process and the maximum posteriori probability assignment method are adopted to enhance the correlation between attribute labels and pixels, and the final attribute recognition results are obtained. Experiments on the benchmark dataset show that the performance of our model is improved, and solves the problems of inaccurate clothing label recognition and pixel resolution area deviation in a single recognition mode.

中文翻译：

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基于姿态估计和多特征融合的人体属性识别方法

作为易于搜索的语义信息，人体服装属性在计算机视觉领域具有重要的研究价值。现有的属性识别方法存在环境因素干扰等问题，导致服装定位精度较差。针对这些问题，提出了一种基于人体姿态估计和多特征融合的人体属性识别方法。首先，通过外观特征匹配得到一些检索结果，用于后续的属性识别。然后，通过基于深度SSD的人体姿态估计方法，定位图像中属于人类的前景区域，排除背景干扰。最后综合各种方法的分析结果。采用迭代平滑过程和最大后验概率分配方法来增强属性标签和像素之间的相关性，得到最终的属性识别结果。在基准数据集上的实验表明，我们的模型的性能有所提高，解决了单一识别模式下服装标签识别不准确和像素分辨率区域偏差的问题。