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Pose Switch-based Convolutional Neural Network for Clothing Analysis in Visual Surveillance Environment
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Datum
2019
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Gesellschaft für Informatik e.V.
Zusammenfassung
Recognizing pedestrian clothing types and styles in outdoor scenes and totally uncontrolled
conditions is appealing to emerging applications such as security, intelligent customer profile
analysis and computer-aided fashion design. Recognition of clothing categories from videos remains
a challenge, mainly due to the poor data resolution and the data covariates that compromise the effectiveness
of automated image analysis techniques (e.g., poses, shadows and partial occlusions).
While state-of-the-art methods typically analyze clothing attributes without paying attention to variation
of human poses, here we claim for the importance of a feature representation derived from
human poses to improve classification rate. Estimating the pose of pedestrians is important to fed
guided features into recognizing system. In this paper, we introduce pose switch-based convolutional
neural network for recognizing the types of clothes of pedestrians, using data acquired in crowded
urban environments. In particular, we compare the effectiveness attained when using CNNs without
respect to human poses variant, and assess the improvements in performance attained by pose
feature extraction. The observed results enable us to conclude that pose information can improve
the performance of clothing recognition system. We focus on the key role of pose information in
pedestrian clothing analysis, which can be employed as an interesting topic for further works.