  Occlusion processing is a key issue in pedestrian attribute recognition
(PAR). Nevertheless, several existing video-based PAR methods have not yet
considered occlusion handling in depth. In this paper, we formulate finding
non-occluded frames as sparsity-based temporal attention of a crowded video. In
this manner, a model is guided not to pay attention to the occluded frame.
However, temporal sparsity cannot include a correlation between attributes when
occlusion occurs. For example, "boots" and "shoe color" cannot be recognized
when the foot is invisible. To solve the uncorrelated attention issue, we also
propose a novel group sparsity-based temporal attention module. Group sparsity
is applied across attention weights in correlated attributes. Thus, attention
weights in a group are forced to pay attention to the same frames. Experimental
results showed that the proposed method achieved a higher F1-score than the
state-of-the-art methods on two video-based PAR datasets.
