Human attribute recognition method based on pose estimation and multiple-feature fusion

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.