Pose Switch-based Convolutional Neural Network for Clothing Analysis in Visual Surveillance Environment

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.