Pedestrian attribute recognition, which can benefit other tasks such as person re-identification and pedestrian retrieval, is very important in video surveillance related tasks. In this paper, we observe that the existing methods tackle this problem from the perspective of multi-label classification without considering the hierarchical relationships among the attributes. In human cognition, the attributes can be categorized according to their semantic/abstraction levels. The high-level attributes can be predicted by reasoning from the low-level and medium-level attributes, while the recognition of the low-level and medium level attributes can be guided by the high-level attributes. Based on this attribute categorization, we propose a novel Hierarchical Reasoning Network (HR-Net), which can hierarchically predict the attributes at different abstraction levels in different stages of the network. We also propose an attribute reasoning structure to exploit the relationships among the attributes at different semantic levels. Experimental results demonstrate that the proposed network gives superior performances compared to the state-of-the-art techniques.

To address challenges related to the inadequate representation and inaccurate discrimination of pedestrian attributes, we propose a novel method for person re-identification, which leverages global feature learning and classification optimization. Specifically, this approach integrates a Normalization-based Channel Attention Module into the fundamental ResNet50 backbone, utilizing a scaling factor to prioritize and enhance key pedestrian feature information. Furthermore, dynamic activation functions are employed to adaptively modulate the parameters of ReLU based on the input convolutional feature maps, thereby bolstering the nonlinear expression capabilities of the network model. By incorporating Arcface loss into the cross-entropy loss, the supervised model is trained to learn pedestrian features that exhibit significant inter-class variance while maintaining tight intra-class coherence. The evaluation of the enhanced model on two popular datasets, Market1501 and DukeMTMC-ReID, reveals improvements in Rank-1 accuracy by 1.28% and 1.4%, respectively, along with corresponding gains in the mean average precision (mAP) of 1.93% and 1.84%. These findings indicate that the proposed model is capable of extracting more robust pedestrian features, enhancing feature discriminability, and ultimately achieving superior recognition accuracy.