Pedestrian Attribute Recognition (PAR) plays an important role in intelligent video surveillance. This paper tackles two severe challenges in it i.e., complex relations between images and attributes, and imbalanced distribution of pedestrian attributes. Specifically, a new multiple time steps attention mechanism is proposed to boost the modeling of the relations. Different from existing attention approaches that only focus on the current and previous time steps, it also exploits the knowledge of next time step. By adaptively capturing the knowledge of multiple time steps, more contextual knowledge is exploited. Meanwhile, to alleviate the challenge of imbalanced distribution of pedestrian attributes, a focal balance loss function is developed by increasing the cost of those attributes difficult to recognize. The proposed framework is dubbed as MTA-Net, which is demonstrated to be effective on two benchmark datasets, i.e., PETA and RAP.

行人属性识别（PAR）在智能视频监控中起着重要作用。本文解决了其中的两个严峻挑战，即图像和属性之间的复杂关系以及行人属性的不平衡分布。具体而言，提出了一种新的多时间步注意机制，以促进关系的建模。与仅关注当前和先前时间步骤的现有注意力方法不同，它还利用了下一时间步骤的知识。通过自适应地捕获多个时间步长的知识，可以利用更多的上下文知识。同时，为了减轻行人属性分布不均衡的挑战，通过增加难以识别的那些属性的成本来开发焦点平衡损失功能。