@article{ref1,
title="Occluded pedestrian-attribute recognition for video sensors using group sparsity",
journal="Sensors (Basel)",
year="2022",
author="Lee, Geonu and Yun, Kimin and Cho, Jungchan",
volume="22",
number="17",
pages="e6626-e6626",
abstract="Pedestrians are often obstructed by other objects or people in real-world vision sensors. These obstacles make pedestrian-attribute recognition (PAR) difficult; hence, occlusion processing for visual sensing is a key issue in PAR. To address this problem, we first formulate the identification of non-occluded frames as temporal attention based on the sparsity of a crowded video. In other words, a model for PAR is guided to prevent paying attention to the occluded frame. However, we deduced that this approach cannot include a correlation between attributes when occlusion occurs. For example, &quot;boots&quot; and &quot;shoe color&quot; cannot be recognized simultaneously when the foot is invisible. To address the uncorrelated attention issue, we propose a novel temporal-attention module based on group sparsity. Group sparsity is applied across attention weights in correlated attributes. Accordingly, physically-adjacent pedestrian attributes are grouped, and the attention weights of a group are forced to focus on the same frames. Experimental results indicate that the proposed method achieved 1.18% and 6.21% higher F1-scores than the advanced baseline method on the occlusion samples in DukeMTMC-VideoReID and MARS video-based PAR datasets, respectively.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22176626",
url="http://dx.doi.org/10.3390/s22176626"
}