Existing pedestrian attribute recognition (PAR) algorithms are mainly developed based on a static image. However, the performance is not reliable for images with challenging factors, such as heavy occlusion, motion blur, etc. In this work, we propose to understand human attributes using video frames that can make full use of temporal information. Specifically, we formulate the video-based PAR as a vision-language fusion problem and adopt pre-trained big models CLIP to extract the feature embeddings of given video frames. To better utilize the semantic information, we take the attribute list as another input and transform the attribute words/phrase into the corresponding sentence via split, expand, and prompt. Then, the text encoder of CLIP is utilized for language embedding. The averaged visual tokens and text tokens are concatenated and fed into a fusion Transformer for multi-modal interactive learning. The enhanced tokens will be fed into a classification head for pedestrian attribute prediction. Extensive experiments on a large-scale video-based PAR dataset fully validated the effectiveness of our proposed framework.

翻译：现有行人属性识别算法主要基于静态图像开发。然而，在严重遮挡、运动模糊等具有挑战性的图像上，其性能不可靠。本工作提出利用视频帧理解人体属性，从而充分利用时序信息。具体而言，我们将视频行人属性识别构建为视觉-语言融合问题，并采用预训练大模型CLIP提取给定视频帧的特征嵌入。为更好利用语义信息，我们将属性列表作为另一输入，通过拆分、扩展和提示将属性词/短语转换为对应句子。随后，利用CLIP文本编码器进行语言嵌入。平均视觉令牌与文本令牌串联后输入融合Transformer进行多模态交互学习。增强后的令牌将被送入分类头进行行人属性预测。在大型视频行人属性识别数据集上的大量实验充分验证了所提框架的有效性。